This notebook contains the code to reproduce the main figures of the manuscript titled "Blood RNA Signatures Predict Recent Tuberculosis Exposure in Mice, Macaques and Humans"¶
Only Ingenuity Pathway Analysis for Figure 5E is excluded (commercial software)
It is recommended that jupyter extensions are used to allow easy display and review of the code:
- https://github.com/ipython-contrib/jupyter_contrib_nbextensions
All code was run with access to a 128 GB RAM machine with 48 CPU cores. This amount of memory and CPU-cores is not necessary for all calculations, but may be necessary to some of the calls to the caret package (machine learning hyperparameter searches).
All code written during the development of this project is in "Time Since Infection RNA_Paper_Analysis.ipynb" and may be reviewed. However, it is not structured for easy, streamlined running of all code.
There are very small differences in p-values between Figures 2-4 (A,E only for Figure 4) herein compared to the original analysis reported in the paper, due the absence of setting a random seed before several random hyperparameter searches on training sets. The ultimate results are insensitive to these very small differences in random parameter settings.
In [9]:
path="." # this is the TB repository folder
The following code shows the software packages installed in the environment used to produce the analyses of the paper. In each section of the analysis, code is provided for installing needed R dependencies¶
R environment (R 3.4.3)¶
In [2]:
ipacks = installed.packages()
ipacks
# To access a specific package's version info:
ipacks["pROC",]
| Package | LibPath | Version | Priority | Depends | Imports | LinkingTo | Suggests | Enhances | License | License_is_FOSS | License_restricts_use | OS_type | MD5sum | NeedsCompilation | Built | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| acepack | acepack | /master/rault/anaconda3/envs/TB/lib/R/library | 1.4.1 | NA | NA | NA | NA | testthat | NA | MIT + file LICENSE | NA | NA | NA | NA | yes | 3.4.3 |
| akima | akima | /master/rault/anaconda3/envs/TB/lib/R/library | 0.6-2 | NA | R (>= 2.0.0) | sp | NA | NA | NA | ACM | file LICENSE | NA | yes | NA | NA | yes | 3.4.3 |
| annotate | annotate | /master/rault/anaconda3/envs/TB/lib/R/library | 1.56.2 | NA | R (>= 2.10), AnnotationDbi (>= 1.27.5), XML | Biobase, DBI, xtable, graphics, utils, stats, methods, BiocGenerics (>= 0.13.8), RCurl | NA | hgu95av2.db, genefilter, Biostrings (>= 2.25.10), IRanges, rae230a.db, rae230aprobe, tkWidgets, GO.db, org.Hs.eg.db, org.Mm.eg.db, hom.Hs.inp.db, humanCHRLOC, Rgraphviz, RUnit, | NA | Artistic-2.0 | NA | NA | NA | NA | no | 3.4.3 |
| AnnotationDbi | AnnotationDbi | /master/rault/anaconda3/envs/TB/lib/R/library | 1.40.0 | NA | R (>= 2.7.0), methods, utils, stats4, BiocGenerics (>= 0.23.1), Biobase (>= 1.17.0), IRanges | methods, utils, DBI, RSQLite, stats4, BiocGenerics, Biobase, S4Vectors (>= 0.9.25), IRanges | NA | DBI (>= 0.2-4), RSQLite (>= 0.6-4), hgu95av2.db, GO.db, org.Sc.sgd.db, org.At.tair.db, KEGG.db, RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene, hom.Hs.inp.db, org.Hs.eg.db, reactome.db, AnnotationForge, graph, EnsDb.Hsapiens.v75, BiocStyle, knitr | NA | Artistic-2.0 | NA | NA | NA | NA | no | 3.4.3 |
| assertthat | assertthat | /master/rault/anaconda3/envs/TB/lib/R/library | 0.2.0 | NA | NA | tools | NA | testthat | NA | GPL-3 | NA | NA | NA | NA | no | 3.4.3 |
| backports | backports | /master/rault/anaconda3/envs/TB/lib/R/library | 1.1.2 | NA | R (>= 3.0.0) | utils | NA | NA | NA | GPL-2 | NA | NA | NA | NA | yes | 3.4.3 |
| base | base | /master/rault/anaconda3/envs/TB/lib/R/library | 3.4.3 | base | NA | NA | NA | methods | NA | Part of R 3.4.3 | NA | NA | NA | NA | NA | 3.4.3 |
| base64 | base64 | /master/rault/anaconda3/envs/TB/lib/R/library | 2.0 | NA | NA | openssl | NA | NA | NA | MIT + file LICENSE | NA | NA | NA | NA | no | 3.4.3 |
| base64enc | base64enc | /master/rault/anaconda3/envs/TB/lib/R/library | 0.1-3 | NA | R (>= 2.9.0) | NA | NA | NA | png | GPL-2 | GPL-3 | NA | NA | NA | NA | yes | 3.4.3 |
| BH | BH | /master/rault/anaconda3/envs/TB/lib/R/library | 1.65.0-1 | NA | NA | NA | NA | NA | NA | BSL-1.0 | NA | NA | NA | NA | no | 3.4.3 |
| bindr | bindr | /master/rault/anaconda3/envs/TB/lib/R/library | 0.1 | NA | NA | NA | NA | testthat | NA | MIT + file LICENSE | NA | NA | NA | NA | no | 3.4.3 |
| bindrcpp | bindrcpp | /master/rault/anaconda3/envs/TB/lib/R/library | 0.2 | NA | NA | Rcpp, bindr | Rcpp, plogr | testthat | NA | MIT + file LICENSE | NA | NA | NA | NA | yes | 3.4.3 |
| Biobase | Biobase | /master/rault/anaconda3/envs/TB/lib/R/library | 2.38.0 | NA | R (>= 2.10), BiocGenerics (>= 0.3.2), utils | methods | NA | tools, tkWidgets, ALL, RUnit, golubEsets | NA | Artistic-2.0 | NA | NA | NA | NA | yes | 3.4.3 |
| BiocGenerics | BiocGenerics | /master/rault/anaconda3/envs/TB/lib/R/library | 0.24.0 | NA | methods, utils, graphics, stats, parallel | methods, utils, graphics, stats, parallel | NA | Biobase, S4Vectors, IRanges, GenomicRanges, AnnotationDbi, oligoClasses, oligo, affyPLM, flowClust, affy, DESeq2, MSnbase, annotate, RUnit | NA | Artistic-2.0 | NA | NA | NA | NA | no | 3.4.3 |
| BiocInstaller | BiocInstaller | /master/rault/anaconda3/envs/TB/lib/R/library | 1.28.0 | NA | R (>= 3.4.0) | NA | NA | devtools, RUnit, BiocGenerics | NA | Artistic-2.0 | NA | NA | NA | NA | no | 3.4.3 |
| BiocParallel | BiocParallel | /master/rault/anaconda3/envs/TB/lib/R/library | 1.12.0 | NA | methods | stats, utils, futile.logger, parallel, snow | BH | BiocGenerics, tools, foreach, BatchJobs, BBmisc, doParallel, Rmpi, GenomicRanges, RNAseqData.HNRNPC.bam.chr14, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, Rsamtools, GenomicAlignments, ShortRead, codetools, RUnit, BiocStyle, knitr | NA | GPL-2 | GPL-3 | NA | NA | NA | NA | yes | 3.4.3 |
| biomaRt | biomaRt | /master/rault/anaconda3/envs/TB/lib/R/library | 2.34.2 | NA | methods | utils, XML, RCurl, AnnotationDbi, progress, stringr, httr | NA | annotate, BiocStyle, knitr, rmarkdown, testthat | NA | Artistic-2.0 | NA | NA | NA | NA | no | 3.4.3 |
| bit | bit | /master/rault/anaconda3/envs/TB/lib/R/library | 1.1-12 | NA | R (>= 2.9.2) | NA | NA | NA | NA | GPL-2 | NA | NA | NA | NA | yes | 3.4.3 |
| bit64 | bit64 | /master/rault/anaconda3/envs/TB/lib/R/library | 0.9-7 | NA | R (>= 3.0.1), bit (>= 1.1-12), utils, methods, stats | NA | NA | NA | NA | GPL-2 | NA | NA | NA | NA | yes | 3.4.3 |
| bitops | bitops | /master/rault/anaconda3/envs/TB/lib/R/library | 1.0-6 | NA | NA | NA | NA | NA | NA | GPL (>= 2) | NA | NA | NA | NA | yes | 3.4.3 |
| blob | blob | /master/rault/anaconda3/envs/TB/lib/R/library | 1.1.1 | NA | NA | methods, prettyunits | NA | covr, pillar (>= 1.2.1), testthat | NA | GPL-3 | NA | NA | NA | NA | no | 3.4.3 |
| bmp | bmp | /master/rault/anaconda3/envs/TB/lib/R/library | 0.3 | NA | NA | NA | NA | pixmap, testthat | NA | GPL (>= 2) | NA | NA | NA | NA | no | 3.4.3 |
| boot | boot | /master/rault/anaconda3/envs/TB/lib/R/library | 1.3-20 | recommended | R (>= 3.0.0), graphics, stats | NA | NA | MASS, survival | NA | Unlimited | NA | NA | NA | NA | no | 3.4.3 |
| brew | brew | /master/rault/anaconda3/envs/TB/lib/R/library | 1.0-6 | NA | NA | NA | NA | NA | NA | GPL-2 | NA | NA | NA | NA | NA | 3.4.3 |
| broom | broom | /master/rault/anaconda3/envs/TB/lib/R/library | 0.4.3 | NA | NA | plyr, dplyr, tidyr, psych, stringr, reshape2, nlme, methods | NA | knitr, boot, survival, gam, glmnet, lfe, Lahman, MASS, sp, maps, maptools, multcomp, testthat, lme4, zoo, lmtest, plm, biglm, ggplot2, nnet, geepack, AUC, ergm, network, statnet.common, xergm, btergm, binGroup, Hmisc, bbmle, gamlss, rstan, rstanarm, brms, coda, gmm, Matrix, ks, purrr, orcutt, mgcv, lmodel2, poLCA, mclust, covr, lsmeans, emmeans, betareg, robust, akima, AER, muhaz, speedglm, tibble | NA | MIT + file LICENSE | NA | NA | NA | NA | no | 3.4.3 |
| Cairo | Cairo | /master/rault/anaconda3/envs/TB/lib/R/library | 1.5-9 | NA | R (>= 2.4.0) | grDevices, graphics | NA | png | FastRWeb | GPL-2 | NA | NA | NA | NA | yes | 3.4.3 |
| callr | callr | /master/rault/anaconda3/envs/TB/lib/R/library | 1.0.0 | NA | NA | NA | NA | covr, testthat | NA | MIT + file LICENSE | NA | NA | NA | NA | no | 3.4.3 |
| caret | caret | /master/rault/anaconda3/envs/TB/lib/R/library | 6.0-78 | NA | R (>= 2.10), lattice (>= 0.20), ggplot2 | foreach, methods, plyr, ModelMetrics (>= 1.1.0), nlme, reshape2, stats, stats4, utils, grDevices, recipes (>= 0.0.1), withr (>= 2.0.0) | NA | BradleyTerry2, e1071, earth (>= 2.2-3), fastICA, gam, ipred, kernlab, klaR, MASS, ellipse, mda, mgcv, mlbench, MLmetrics, nnet, party (>= 0.9-99992), pls, pROC, proxy, randomForest, RANN, spls, subselect, pamr, superpc, Cubist, testthat (>= 0.9.1) | NA | GPL (>= 2) | NA | NA | NA | NA | yes | 3.4.3 |
| caTools | caTools | /master/rault/anaconda3/envs/TB/lib/R/library | 1.17.1 | NA | R (>= 2.2.0) | bitops | NA | MASS, rpart | NA | GPL-3 | NA | NA | NA | NA | yes | 3.4.3 |
| cellranger | cellranger | /master/rault/anaconda3/envs/TB/lib/R/library | 1.1.0 | NA | R (>= 3.0.0) | rematch, tibble | NA | covr, testthat (>= 1.0.0), knitr, rmarkdown | NA | MIT + file LICENSE | NA | NA | NA | NA | no | 3.4.3 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| tcltk | tcltk | /master/rault/anaconda3/envs/TB/lib/R/library | 3.4.3 | base | NA | utils | NA | NA | NA | Part of R 3.4.3 | NA | NA | NA | NA | yes | 3.4.3 |
| tensorflow | tensorflow | /master/rault/anaconda3/envs/TB/lib/R/library | 1.4.3 | NA | R (>= 3.1) | config, jsonlite (>= 1.2), processx, reticulate (>= 1.3), tfruns (>= 1.0), utils, yaml, rstudioapi (>= 0.7) | NA | testthat | NA | Apache License 2.0 | NA | NA | NA | NA | no | 3.4.3 |
| testthat | testthat | /master/rault/anaconda3/envs/TB/lib/R/library | 2.0.0 | NA | R (>= 3.1) | cli, crayon, digest, magrittr, methods, praise, R6 (>= 2.2.0), rlang, withr (>= 2.0.0) | NA | covr, devtools, knitr, rmarkdown, xml2 | NA | MIT + file LICENSE | NA | NA | NA | NA | yes | 3.4.3 |
| tfruns | tfruns | /master/rault/anaconda3/envs/TB/lib/R/library | 1.1 | NA | R (>= 3.1) | utils, jsonlite (>= 1.2), base64enc, yaml, config, magrittr, tibble, whisker, tidyselect, rlang, rstudioapi (>= 0.7), reticulate | NA | testthat, knitr | NA | Apache License 2.0 | NA | NA | NA | NA | no | 3.4.3 |
| tibble | tibble | /master/rault/anaconda3/envs/TB/lib/R/library | 1.4.1 | NA | R (>= 3.1.0) | crayon, methods, pillar, rlang, utils | NA | covr, dplyr, import, knitr (>= 1.5.32), microbenchmark, mockr, nycflights13, testthat, rmarkdown, withr | NA | MIT + file LICENSE | NA | NA | NA | NA | yes | 3.4.3 |
| tidyr | tidyr | /master/rault/anaconda3/envs/TB/lib/R/library | 0.7.2 | NA | R (>= 3.1.0) | dplyr (>= 0.7.0), glue, magrittr, purrr, rlang, Rcpp, stringi, tibble, tidyselect | Rcpp | knitr, testthat, covr, gapminder, rmarkdown | NA | MIT + file LICENSE | NA | NA | NA | NA | yes | 3.4.3 |
| tidyselect | tidyselect | /master/rault/anaconda3/envs/TB/lib/R/library | 0.2.3 | NA | R (>= 3.1.0) | glue, purrr, rlang (>= 0.1), Rcpp (>= 0.12.0) | Rcpp (>= 0.12.0), | dplyr, testthat | NA | GPL-3 | NA | NA | NA | NA | yes | 3.4.3 |
| tidyverse | tidyverse | /master/rault/anaconda3/envs/TB/lib/R/library | 1.2.1 | NA | NA | broom (>= 0.4.2), cli (>= 1.0.0), crayon (>= 1.3.4), dplyr (>= 0.7.4), dbplyr (>= 1.1.0), forcats (>= 0.2.0), ggplot2 (>= 2.2.1), haven (>= 1.1.0), hms (>= 0.3), httr (>= 1.3.1), jsonlite (>= 1.5), lubridate (>= 1.7.1), magrittr (>= 1.5), modelr (>= 0.1.1), purrr (>= 0.2.4), readr (>= 1.1.1), readxl (>= 1.0.0), reprex (>= 0.1.1), rlang (>= 0.1.4), rstudioapi (>= 0.7), rvest (>= 0.3.2), stringr (>= 1.2.0), tibble (>= 1.3.4), tidyr (>= 0.7.2), xml2 (>= 1.1.1) | NA | feather (>= 0.3.1), knitr (>= 1.17), rmarkdown (>= 1.7.4) | NA | GPL-3 | file LICENSE | NA | NA | NA | NA | no | 3.4.3 |
| timeDate | timeDate | /master/rault/anaconda3/envs/TB/lib/R/library | 3042.101 | NA | R (>= 2.15.1), graphics, utils, stats, methods | NA | NA | date, RUnit | NA | GPL (>= 2) | NA | NA | NA | NA | no | 3.4.3 |
| tools | tools | /master/rault/anaconda3/envs/TB/lib/R/library | 3.4.3 | base | NA | NA | NA | codetools, methods, xml2, curl | NA | Part of R 3.4.3 | NA | NA | NA | NA | yes | 3.4.3 |
| TTR | TTR | /master/rault/anaconda3/envs/TB/lib/R/library | 0.23-2 | NA | NA | xts (>= 0.10-0), zoo, curl | xts | RUnit | quantmod | GPL-2 | NA | NA | NA | NA | yes | 3.4.3 |
| utf8 | utf8 | /master/rault/anaconda3/envs/TB/lib/R/library | 1.1.2 | NA | R (>= 2.10) | NA | NA | corpus, knitr, testthat | NA | Apache License (== 2.0) | file LICENSE | NA | NA | NA | NA | yes | 3.4.3 |
| utils | utils | /master/rault/anaconda3/envs/TB/lib/R/library | 3.4.3 | base | NA | NA | NA | methods, XML | NA | Part of R 3.4.3 | NA | NA | NA | NA | yes | 3.4.3 |
| uuid | uuid | /master/rault/anaconda3/envs/TB/lib/R/library | 0.1-2 | NA | R (>= 2.9.0) | NA | NA | NA | NA | MIT + file LICENSE | NA | NA | NA | NA | yes | 3.4.3 |
| vcd | vcd | /master/rault/anaconda3/envs/TB/lib/R/library | 1.4-4 | NA | R (>= 2.4.0), grid | stats, utils, MASS, grDevices, colorspace, lmtest | NA | KernSmooth, mvtnorm, kernlab, HSAUR, coin | NA | GPL-2 | NA | NA | NA | NA | no | 3.4.3 |
| verification | verification | /master/rault/anaconda3/envs/TB/lib/R/library | 1.42 | NA | R (>= 2.10), methods, fields, boot, CircStats, MASS, dtw | graphics, stats | NA | NA | NA | GPL (>= 2) | NA | NA | NA | NA | no | 3.4.3 |
| viridis | viridis | /master/rault/anaconda3/envs/TB/lib/R/library | 0.4.0 | NA | R (>= 2.10), viridisLite (>= 0.2.0) | stats, ggplot2 (>= 1.0.1), gridExtra | NA | hexbin (>= 1.27.0), scales, MASS, knitr, dichromat, colorspace, rasterVis, httr, mapproj, vdiffr, svglite (>= 1.2.0), testthat, covr, rmarkdown | NA | MIT + file LICENSE | NA | NA | NA | NA | no | 3.4.3 |
| viridisLite | viridisLite | /master/rault/anaconda3/envs/TB/lib/R/library | 0.2.0 | NA | R (>= 2.10) | NA | NA | hexbin (>= 1.27.0), ggplot2 (>= 1.0.1), testthat, covr | NA | MIT + file LICENSE | NA | NA | NA | NA | no | 3.4.3 |
| visNetwork | visNetwork | /master/rault/anaconda3/envs/TB/lib/R/library | 2.0.2 | NA | R (>= 3.0) | htmlwidgets, htmltools, jsonlite, magrittr, utils, methods, grDevices, stats | NA | knitr, igraph, rpart, shiny, shinyWidgets, colourpicker, sparkline, ggraph, flashClust | NA | MIT + file LICENSE | NA | NA | NA | NA | no | 3.4.3 |
| whisker | whisker | /master/rault/anaconda3/envs/TB/lib/R/library | 0.3-2 | NA | NA | NA | NA | markdown | NA | GPL-3 | NA | NA | NA | NA | no | 3.4.3 |
| withr | withr | /master/rault/anaconda3/envs/TB/lib/R/library | 2.1.1 | NA | R (>= 3.0.2) | stats, graphics, grDevices | NA | testthat, covr, lattice, DBI, RSQLite, methods, knitr, rmarkdown | NA | GPL (>= 2) | NA | NA | NA | NA | no | 3.4.3 |
| xgboost | xgboost | /master/rault/anaconda3/envs/TB/lib/R/library | 0.6-4 | NA | R (>= 3.3.0) | Matrix (>= 1.1-0), methods, data.table (>= 1.9.6), magrittr (>= 1.5), stringi (>= 0.5.2) | NA | knitr, rmarkdown, ggplot2 (>= 1.0.1), DiagrammeR (>= 0.9.0), Ckmeans.1d.dp (>= 3.3.1), vcd (>= 1.3), testthat, igraph (>= 1.0.1) | NA | Apache License (== 2.0) | file LICENSE | NA | NA | NA | NA | yes | 3.4.3 |
| XML | XML | /master/rault/anaconda3/envs/TB/lib/R/library | 3.98-1.11 | NA | R (>= 2.13.0), methods, utils | NA | NA | bitops, RCurl | NA | BSD_2_clause + file LICENSE | NA | NA | NA | NA | yes | 3.4.3 |
| xml2 | xml2 | /master/rault/anaconda3/envs/TB/lib/R/library | 1.1.1 | NA | R (>= 3.1.0) | Rcpp | Rcpp (>= 0.11.4.6), BH | testthat, curl, covr, knitr, rmarkdown, magrittr, httr | NA | GPL (>= 2) | NA | NA | NA | NA | yes | 3.4.3 |
| xtable | xtable | /master/rault/anaconda3/envs/TB/lib/R/library | 1.8-2 | NA | R (>= 2.10.0) | stats, utils | NA | knitr, lsmeans, spdep, splm, sphet, plm, zoo, survival | NA | GPL (>= 2) | NA | NA | NA | NA | no | 3.4.3 |
| xts | xts | /master/rault/anaconda3/envs/TB/lib/R/library | 0.10-1 | NA | zoo (>= 1.7-12) | methods | zoo | timeSeries, timeDate, tseries, chron, fts, tis, RUnit | NA | GPL (>= 2) | NA | NA | NA | NA | yes | 3.4.3 |
| yaml | yaml | /master/rault/anaconda3/envs/TB/lib/R/library | 2.1.16 | NA | NA | NA | NA | testthat | NA | BSD_3_clause + file LICENSE | NA | NA | NA | NA | yes | 3.4.3 |
| zeallot | zeallot | /master/rault/anaconda3/envs/TB/lib/R/library | 0.0.6 | NA | NA | NA | NA | testthat, knitr, rmarkdown, purrr, magrittr | NA | MIT + file LICENSE | NA | NA | NA | NA | no | 3.4.3 |
| zlibbioc | zlibbioc | /master/rault/anaconda3/envs/TB/lib/R/library | 1.24.0 | NA | NA | NA | NA | NA | NA | Artistic-2.0 + file LICENSE | NA | NA | NA | NA | yes | 3.4.3 |
| zoo | zoo | /master/rault/anaconda3/envs/TB/lib/R/library | 1.8-0 | NA | R (>= 2.10.0), stats | utils, graphics, grDevices, lattice (>= 0.20-27) | NA | coda, chron, DAAG, fts, ggplot2, mondate, scales, strucchange, timeDate, timeSeries, tis, tseries, xts | NA | GPL-2 | GPL-3 | NA | NA | NA | NA | yes | 3.4.3 |
- Package
- 'pROC'
- LibPath
- '/master/rault/anaconda3/envs/TB/lib/R/library'
- Version
- '1.12.0'
- Priority
- NA
- Depends
- 'R (>= 2.14)'
- Imports
- 'plyr, utils, methods, Rcpp (>= 0.11.1), ggplot2,'
- LinkingTo
- 'Rcpp'
- Suggests
- 'microbenchmark, tcltk, MASS, logcondens, doParallel,\ntestthat, vdiffr'
- Enhances
- NA
- License
- 'GPL (>= 3)'
- License_is_FOSS
- NA
- License_restricts_use
- NA
- OS_type
- NA
- MD5sum
- NA
- NeedsCompilation
- 'yes'
- Built
- '3.4.3'
In [3]:
print(ipacks)
Package
acepack "acepack"
akima "akima"
annotate "annotate"
AnnotationDbi "AnnotationDbi"
assertthat "assertthat"
backports "backports"
base "base"
base64 "base64"
base64enc "base64enc"
BH "BH"
bindr "bindr"
bindrcpp "bindrcpp"
Biobase "Biobase"
BiocGenerics "BiocGenerics"
BiocInstaller "BiocInstaller"
BiocParallel "BiocParallel"
biomaRt "biomaRt"
bit "bit"
bit64 "bit64"
bitops "bitops"
blob "blob"
bmp "bmp"
boot "boot"
brew "brew"
broom "broom"
Cairo "Cairo"
callr "callr"
caret "caret"
caTools "caTools"
cellranger "cellranger"
checkmate "checkmate"
checkpoint "checkpoint"
CircStats "CircStats"
class "class"
cli "cli"
clipr "clipr"
cluster "cluster"
COCONUT "COCONUT"
codetools "codetools"
colorspace "colorspace"
compiler "compiler"
config "config"
cowplot "cowplot"
crayon "crayon"
crosstalk "crosstalk"
curl "curl"
CVST "CVST"
data.table "data.table"
datasets "datasets"
DBI "DBI"
dbplyr "dbplyr"
ddalpha "ddalpha"
debugme "debugme"
DeconRNASeq "DeconRNASeq"
deepnet "deepnet"
DEoptimR "DEoptimR"
deployrRserve "deployrRserve"
DiagrammeR "DiagrammeR"
dichromat "dichromat"
digest "digest"
dimRed "dimRed"
doParallel "doParallel"
dotCall64 "dotCall64"
downloader "downloader"
dplyr "dplyr"
DRR "DRR"
DT "DT"
dtangle "dtangle"
dtw "dtw"
e1071 "e1071"
EpiDISH "EpiDISH"
evaluate "evaluate"
extrafontdb "extrafontdb"
fdrtool "fdrtool"
fields "fields"
FNN "FNN"
fontBitstreamVera "fontBitstreamVera"
fontLiberation "fontLiberation"
fontquiver "fontquiver"
forcats "forcats"
foreach "foreach"
foreign "foreign"
formatR "formatR"
Formula "Formula"
futile.logger "futile.logger"
futile.options "futile.options"
gbm "gbm"
gdata "gdata"
genefilter "genefilter"
GEOmetadb "GEOmetadb"
GEOquery "GEOquery"
GGally "GGally"
ggplot2 "ggplot2"
ggplotify "ggplotify"
ggpmisc "ggpmisc"
ggpubr "ggpubr"
ggrepel "ggrepel"
ggsci "ggsci"
ggsignif "ggsignif"
glmnet "glmnet"
glue "glue"
gower "gower"
gplots "gplots"
graphics "graphics"
grDevices "grDevices"
grid "grid"
gridExtra "gridExtra"
gridGraphics "gridGraphics"
gtable "gtable"
gtools "gtools"
haven "haven"
hexbin "hexbin"
HGNChelper "HGNChelper"
highr "highr"
Hmisc "Hmisc"
hms "hms"
htmlTable "htmlTable"
htmltools "htmltools"
htmlwidgets "htmlwidgets"
httpuv "httpuv"
httr "httr"
HybridMTest "HybridMTest"
igraph "igraph"
IlluminaDataTestFiles "IlluminaDataTestFiles"
illuminaHumanv4.db "illuminaHumanv4.db"
illuminaio "illuminaio"
influenceR "influenceR"
ipred "ipred"
IRanges "IRanges"
IRdisplay "IRdisplay"
IRkernel "IRkernel"
irlba "irlba"
iterators "iterators"
jpeg "jpeg"
jsonlite "jsonlite"
keras "keras"
kernlab "kernlab"
KernSmooth "KernSmooth"
knitr "knitr"
ks "ks"
labeling "labeling"
lambda.r "lambda.r"
lattice "lattice"
latticeExtra "latticeExtra"
lava "lava"
lazyeval "lazyeval"
limma "limma"
limSolve "limSolve"
lme4 "lme4"
lmerTest "lmerTest"
lmtest "lmtest"
logcondens "logcondens"
lpSolve "lpSolve"
lubridate "lubridate"
magrittr "magrittr"
manhattanly "manhattanly"
maps "maps"
markdown "markdown"
MASS "MASS"
Matrix "Matrix"
matrixStats "matrixStats"
mclust "mclust"
memoise "memoise"
MetaIntegrator "MetaIntegrator"
methods "methods"
Metrics "Metrics"
mgcv "mgcv"
microbenchmark "microbenchmark"
MicrosoftR "MicrosoftR"
mime "mime"
minqa "minqa"
mlbench "mlbench"
mnormt "mnormt"
ModelMetrics "ModelMetrics"
modelr "modelr"
multicool "multicool"
multtest "multtest"
munsell "munsell"
mvtnorm "mvtnorm"
nlme "nlme"
nloptr "nloptr"
nnet "nnet"
numDeriv "numDeriv"
openssl "openssl"
org.Hs.eg.db "org.Hs.eg.db"
parallel "parallel"
pbdZMQ "pbdZMQ"
pcaMethods "pcaMethods"
pheatmap "pheatmap"
pillar "pillar"
pkgconfig "pkgconfig"
plogr "plogr"
plotly "plotly"
plyr "plyr"
png "png"
polynom "polynom"
pracma "pracma"
praise "praise"
preprocessCore "preprocessCore"
prettydoc "prettydoc"
prettyunits "prettyunits"
pROC "pROC"
processx "processx"
prodlim "prodlim"
progress "progress"
proxy "proxy"
psych "psych"
purrr "purrr"
quadprog "quadprog"
quantmod "quantmod"
R.methodsS3 "R.methodsS3"
R.oo "R.oo"
R.utils "R.utils"
R6 "R6"
randomForest "randomForest"
ranger "ranger"
rbokeh "rbokeh"
RColorBrewer "RColorBrewer"
Rcpp "Rcpp"
RcppEigen "RcppEigen"
RcppRoll "RcppRoll"
RCurl "RCurl"
readbitmap "readbitmap"
readr "readr"
readxl "readxl"
recipes "recipes"
rematch "rematch"
repr "repr"
reprex "reprex"
reshape "reshape"
reshape2 "reshape2"
reticulate "reticulate"
RevoIOQ "RevoIOQ"
RevoMods "RevoMods"
RevoUtils "RevoUtils"
RevoUtilsMath "RevoUtilsMath"
rgexf "rgexf"
rhdf5 "rhdf5"
rlang "rlang"
rmarkdown "rmarkdown"
rmeta "rmeta"
Rmisc "Rmisc"
RMySQL "RMySQL"
robustbase "robustbase"
ROCR "ROCR"
Rook "Rook"
rpart "rpart"
rprojroot "rprojroot"
RSQLite "RSQLite"
rstudioapi "rstudioapi"
Rttf2pt1 "Rttf2pt1"
RUnit "RUnit"
rvcheck "rvcheck"
rvest "rvest"
S4Vectors "S4Vectors"
scales "scales"
selectr "selectr"
sfsmisc "sfsmisc"
shiny "shiny"
snow "snow"
snplist "snplist"
sourcetools "sourcetools"
sp "sp"
spam "spam"
spatial "spatial"
splines "splines"
splus2R "splus2R"
stats "stats"
stats4 "stats4"
stringi "stringi"
stringr "stringr"
survival "survival"
sva "sva"
tcltk "tcltk"
tensorflow "tensorflow"
testthat "testthat"
tfruns "tfruns"
tibble "tibble"
tidyr "tidyr"
tidyselect "tidyselect"
tidyverse "tidyverse"
timeDate "timeDate"
tools "tools"
TTR "TTR"
utf8 "utf8"
utils "utils"
uuid "uuid"
vcd "vcd"
verification "verification"
viridis "viridis"
viridisLite "viridisLite"
visNetwork "visNetwork"
whisker "whisker"
withr "withr"
xgboost "xgboost"
XML "XML"
xml2 "xml2"
xtable "xtable"
xts "xts"
yaml "yaml"
zeallot "zeallot"
zlibbioc "zlibbioc"
zoo "zoo"
LibPath
acepack "/master/rault/anaconda3/envs/TB/lib/R/library"
akima "/master/rault/anaconda3/envs/TB/lib/R/library"
annotate "/master/rault/anaconda3/envs/TB/lib/R/library"
AnnotationDbi "/master/rault/anaconda3/envs/TB/lib/R/library"
assertthat "/master/rault/anaconda3/envs/TB/lib/R/library"
backports "/master/rault/anaconda3/envs/TB/lib/R/library"
base "/master/rault/anaconda3/envs/TB/lib/R/library"
base64 "/master/rault/anaconda3/envs/TB/lib/R/library"
base64enc "/master/rault/anaconda3/envs/TB/lib/R/library"
BH "/master/rault/anaconda3/envs/TB/lib/R/library"
bindr "/master/rault/anaconda3/envs/TB/lib/R/library"
bindrcpp "/master/rault/anaconda3/envs/TB/lib/R/library"
Biobase "/master/rault/anaconda3/envs/TB/lib/R/library"
BiocGenerics "/master/rault/anaconda3/envs/TB/lib/R/library"
BiocInstaller "/master/rault/anaconda3/envs/TB/lib/R/library"
BiocParallel "/master/rault/anaconda3/envs/TB/lib/R/library"
biomaRt "/master/rault/anaconda3/envs/TB/lib/R/library"
bit "/master/rault/anaconda3/envs/TB/lib/R/library"
bit64 "/master/rault/anaconda3/envs/TB/lib/R/library"
bitops "/master/rault/anaconda3/envs/TB/lib/R/library"
blob "/master/rault/anaconda3/envs/TB/lib/R/library"
bmp "/master/rault/anaconda3/envs/TB/lib/R/library"
boot "/master/rault/anaconda3/envs/TB/lib/R/library"
brew "/master/rault/anaconda3/envs/TB/lib/R/library"
broom "/master/rault/anaconda3/envs/TB/lib/R/library"
Cairo "/master/rault/anaconda3/envs/TB/lib/R/library"
callr "/master/rault/anaconda3/envs/TB/lib/R/library"
caret "/master/rault/anaconda3/envs/TB/lib/R/library"
caTools "/master/rault/anaconda3/envs/TB/lib/R/library"
cellranger "/master/rault/anaconda3/envs/TB/lib/R/library"
checkmate "/master/rault/anaconda3/envs/TB/lib/R/library"
checkpoint "/master/rault/anaconda3/envs/TB/lib/R/library"
CircStats "/master/rault/anaconda3/envs/TB/lib/R/library"
class "/master/rault/anaconda3/envs/TB/lib/R/library"
cli "/master/rault/anaconda3/envs/TB/lib/R/library"
clipr "/master/rault/anaconda3/envs/TB/lib/R/library"
cluster "/master/rault/anaconda3/envs/TB/lib/R/library"
COCONUT "/master/rault/anaconda3/envs/TB/lib/R/library"
codetools "/master/rault/anaconda3/envs/TB/lib/R/library"
colorspace "/master/rault/anaconda3/envs/TB/lib/R/library"
compiler "/master/rault/anaconda3/envs/TB/lib/R/library"
config "/master/rault/anaconda3/envs/TB/lib/R/library"
cowplot "/master/rault/anaconda3/envs/TB/lib/R/library"
crayon "/master/rault/anaconda3/envs/TB/lib/R/library"
crosstalk "/master/rault/anaconda3/envs/TB/lib/R/library"
curl "/master/rault/anaconda3/envs/TB/lib/R/library"
CVST "/master/rault/anaconda3/envs/TB/lib/R/library"
data.table "/master/rault/anaconda3/envs/TB/lib/R/library"
datasets "/master/rault/anaconda3/envs/TB/lib/R/library"
DBI "/master/rault/anaconda3/envs/TB/lib/R/library"
dbplyr "/master/rault/anaconda3/envs/TB/lib/R/library"
ddalpha "/master/rault/anaconda3/envs/TB/lib/R/library"
debugme "/master/rault/anaconda3/envs/TB/lib/R/library"
DeconRNASeq "/master/rault/anaconda3/envs/TB/lib/R/library"
deepnet "/master/rault/anaconda3/envs/TB/lib/R/library"
DEoptimR "/master/rault/anaconda3/envs/TB/lib/R/library"
deployrRserve "/master/rault/anaconda3/envs/TB/lib/R/library"
DiagrammeR "/master/rault/anaconda3/envs/TB/lib/R/library"
dichromat "/master/rault/anaconda3/envs/TB/lib/R/library"
digest "/master/rault/anaconda3/envs/TB/lib/R/library"
dimRed "/master/rault/anaconda3/envs/TB/lib/R/library"
doParallel "/master/rault/anaconda3/envs/TB/lib/R/library"
dotCall64 "/master/rault/anaconda3/envs/TB/lib/R/library"
downloader "/master/rault/anaconda3/envs/TB/lib/R/library"
dplyr "/master/rault/anaconda3/envs/TB/lib/R/library"
DRR "/master/rault/anaconda3/envs/TB/lib/R/library"
DT "/master/rault/anaconda3/envs/TB/lib/R/library"
dtangle "/master/rault/anaconda3/envs/TB/lib/R/library"
dtw "/master/rault/anaconda3/envs/TB/lib/R/library"
e1071 "/master/rault/anaconda3/envs/TB/lib/R/library"
EpiDISH "/master/rault/anaconda3/envs/TB/lib/R/library"
evaluate "/master/rault/anaconda3/envs/TB/lib/R/library"
extrafontdb "/master/rault/anaconda3/envs/TB/lib/R/library"
fdrtool "/master/rault/anaconda3/envs/TB/lib/R/library"
fields "/master/rault/anaconda3/envs/TB/lib/R/library"
FNN "/master/rault/anaconda3/envs/TB/lib/R/library"
fontBitstreamVera "/master/rault/anaconda3/envs/TB/lib/R/library"
fontLiberation "/master/rault/anaconda3/envs/TB/lib/R/library"
fontquiver "/master/rault/anaconda3/envs/TB/lib/R/library"
forcats "/master/rault/anaconda3/envs/TB/lib/R/library"
foreach "/master/rault/anaconda3/envs/TB/lib/R/library"
foreign "/master/rault/anaconda3/envs/TB/lib/R/library"
formatR "/master/rault/anaconda3/envs/TB/lib/R/library"
Formula "/master/rault/anaconda3/envs/TB/lib/R/library"
futile.logger "/master/rault/anaconda3/envs/TB/lib/R/library"
futile.options "/master/rault/anaconda3/envs/TB/lib/R/library"
gbm "/master/rault/anaconda3/envs/TB/lib/R/library"
gdata "/master/rault/anaconda3/envs/TB/lib/R/library"
genefilter "/master/rault/anaconda3/envs/TB/lib/R/library"
GEOmetadb "/master/rault/anaconda3/envs/TB/lib/R/library"
GEOquery "/master/rault/anaconda3/envs/TB/lib/R/library"
GGally "/master/rault/anaconda3/envs/TB/lib/R/library"
ggplot2 "/master/rault/anaconda3/envs/TB/lib/R/library"
ggplotify "/master/rault/anaconda3/envs/TB/lib/R/library"
ggpmisc "/master/rault/anaconda3/envs/TB/lib/R/library"
ggpubr "/master/rault/anaconda3/envs/TB/lib/R/library"
ggrepel "/master/rault/anaconda3/envs/TB/lib/R/library"
ggsci "/master/rault/anaconda3/envs/TB/lib/R/library"
ggsignif "/master/rault/anaconda3/envs/TB/lib/R/library"
glmnet "/master/rault/anaconda3/envs/TB/lib/R/library"
glue "/master/rault/anaconda3/envs/TB/lib/R/library"
gower "/master/rault/anaconda3/envs/TB/lib/R/library"
gplots "/master/rault/anaconda3/envs/TB/lib/R/library"
graphics "/master/rault/anaconda3/envs/TB/lib/R/library"
grDevices "/master/rault/anaconda3/envs/TB/lib/R/library"
grid "/master/rault/anaconda3/envs/TB/lib/R/library"
gridExtra "/master/rault/anaconda3/envs/TB/lib/R/library"
gridGraphics "/master/rault/anaconda3/envs/TB/lib/R/library"
gtable "/master/rault/anaconda3/envs/TB/lib/R/library"
gtools "/master/rault/anaconda3/envs/TB/lib/R/library"
haven "/master/rault/anaconda3/envs/TB/lib/R/library"
hexbin "/master/rault/anaconda3/envs/TB/lib/R/library"
HGNChelper "/master/rault/anaconda3/envs/TB/lib/R/library"
highr "/master/rault/anaconda3/envs/TB/lib/R/library"
Hmisc "/master/rault/anaconda3/envs/TB/lib/R/library"
hms "/master/rault/anaconda3/envs/TB/lib/R/library"
htmlTable "/master/rault/anaconda3/envs/TB/lib/R/library"
htmltools "/master/rault/anaconda3/envs/TB/lib/R/library"
htmlwidgets "/master/rault/anaconda3/envs/TB/lib/R/library"
httpuv "/master/rault/anaconda3/envs/TB/lib/R/library"
httr "/master/rault/anaconda3/envs/TB/lib/R/library"
HybridMTest "/master/rault/anaconda3/envs/TB/lib/R/library"
igraph "/master/rault/anaconda3/envs/TB/lib/R/library"
IlluminaDataTestFiles "/master/rault/anaconda3/envs/TB/lib/R/library"
illuminaHumanv4.db "/master/rault/anaconda3/envs/TB/lib/R/library"
illuminaio "/master/rault/anaconda3/envs/TB/lib/R/library"
influenceR "/master/rault/anaconda3/envs/TB/lib/R/library"
ipred "/master/rault/anaconda3/envs/TB/lib/R/library"
IRanges "/master/rault/anaconda3/envs/TB/lib/R/library"
IRdisplay "/master/rault/anaconda3/envs/TB/lib/R/library"
IRkernel "/master/rault/anaconda3/envs/TB/lib/R/library"
irlba "/master/rault/anaconda3/envs/TB/lib/R/library"
iterators "/master/rault/anaconda3/envs/TB/lib/R/library"
jpeg "/master/rault/anaconda3/envs/TB/lib/R/library"
jsonlite "/master/rault/anaconda3/envs/TB/lib/R/library"
keras "/master/rault/anaconda3/envs/TB/lib/R/library"
kernlab "/master/rault/anaconda3/envs/TB/lib/R/library"
KernSmooth "/master/rault/anaconda3/envs/TB/lib/R/library"
knitr "/master/rault/anaconda3/envs/TB/lib/R/library"
ks "/master/rault/anaconda3/envs/TB/lib/R/library"
labeling "/master/rault/anaconda3/envs/TB/lib/R/library"
lambda.r "/master/rault/anaconda3/envs/TB/lib/R/library"
lattice "/master/rault/anaconda3/envs/TB/lib/R/library"
latticeExtra "/master/rault/anaconda3/envs/TB/lib/R/library"
lava "/master/rault/anaconda3/envs/TB/lib/R/library"
lazyeval "/master/rault/anaconda3/envs/TB/lib/R/library"
limma "/master/rault/anaconda3/envs/TB/lib/R/library"
limSolve "/master/rault/anaconda3/envs/TB/lib/R/library"
lme4 "/master/rault/anaconda3/envs/TB/lib/R/library"
lmerTest "/master/rault/anaconda3/envs/TB/lib/R/library"
lmtest "/master/rault/anaconda3/envs/TB/lib/R/library"
logcondens "/master/rault/anaconda3/envs/TB/lib/R/library"
lpSolve "/master/rault/anaconda3/envs/TB/lib/R/library"
lubridate "/master/rault/anaconda3/envs/TB/lib/R/library"
magrittr "/master/rault/anaconda3/envs/TB/lib/R/library"
manhattanly "/master/rault/anaconda3/envs/TB/lib/R/library"
maps "/master/rault/anaconda3/envs/TB/lib/R/library"
markdown "/master/rault/anaconda3/envs/TB/lib/R/library"
MASS "/master/rault/anaconda3/envs/TB/lib/R/library"
Matrix "/master/rault/anaconda3/envs/TB/lib/R/library"
matrixStats "/master/rault/anaconda3/envs/TB/lib/R/library"
mclust "/master/rault/anaconda3/envs/TB/lib/R/library"
memoise "/master/rault/anaconda3/envs/TB/lib/R/library"
MetaIntegrator "/master/rault/anaconda3/envs/TB/lib/R/library"
methods "/master/rault/anaconda3/envs/TB/lib/R/library"
Metrics "/master/rault/anaconda3/envs/TB/lib/R/library"
mgcv "/master/rault/anaconda3/envs/TB/lib/R/library"
microbenchmark "/master/rault/anaconda3/envs/TB/lib/R/library"
MicrosoftR "/master/rault/anaconda3/envs/TB/lib/R/library"
mime "/master/rault/anaconda3/envs/TB/lib/R/library"
minqa "/master/rault/anaconda3/envs/TB/lib/R/library"
mlbench "/master/rault/anaconda3/envs/TB/lib/R/library"
mnormt "/master/rault/anaconda3/envs/TB/lib/R/library"
ModelMetrics "/master/rault/anaconda3/envs/TB/lib/R/library"
modelr "/master/rault/anaconda3/envs/TB/lib/R/library"
multicool "/master/rault/anaconda3/envs/TB/lib/R/library"
multtest "/master/rault/anaconda3/envs/TB/lib/R/library"
munsell "/master/rault/anaconda3/envs/TB/lib/R/library"
mvtnorm "/master/rault/anaconda3/envs/TB/lib/R/library"
nlme "/master/rault/anaconda3/envs/TB/lib/R/library"
nloptr "/master/rault/anaconda3/envs/TB/lib/R/library"
nnet "/master/rault/anaconda3/envs/TB/lib/R/library"
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openssl "/master/rault/anaconda3/envs/TB/lib/R/library"
org.Hs.eg.db "/master/rault/anaconda3/envs/TB/lib/R/library"
parallel "/master/rault/anaconda3/envs/TB/lib/R/library"
pbdZMQ "/master/rault/anaconda3/envs/TB/lib/R/library"
pcaMethods "/master/rault/anaconda3/envs/TB/lib/R/library"
pheatmap "/master/rault/anaconda3/envs/TB/lib/R/library"
pillar "/master/rault/anaconda3/envs/TB/lib/R/library"
pkgconfig "/master/rault/anaconda3/envs/TB/lib/R/library"
plogr "/master/rault/anaconda3/envs/TB/lib/R/library"
plotly "/master/rault/anaconda3/envs/TB/lib/R/library"
plyr "/master/rault/anaconda3/envs/TB/lib/R/library"
png "/master/rault/anaconda3/envs/TB/lib/R/library"
polynom "/master/rault/anaconda3/envs/TB/lib/R/library"
pracma "/master/rault/anaconda3/envs/TB/lib/R/library"
praise "/master/rault/anaconda3/envs/TB/lib/R/library"
preprocessCore "/master/rault/anaconda3/envs/TB/lib/R/library"
prettydoc "/master/rault/anaconda3/envs/TB/lib/R/library"
prettyunits "/master/rault/anaconda3/envs/TB/lib/R/library"
pROC "/master/rault/anaconda3/envs/TB/lib/R/library"
processx "/master/rault/anaconda3/envs/TB/lib/R/library"
prodlim "/master/rault/anaconda3/envs/TB/lib/R/library"
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proxy "/master/rault/anaconda3/envs/TB/lib/R/library"
psych "/master/rault/anaconda3/envs/TB/lib/R/library"
purrr "/master/rault/anaconda3/envs/TB/lib/R/library"
quadprog "/master/rault/anaconda3/envs/TB/lib/R/library"
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R.methodsS3 "/master/rault/anaconda3/envs/TB/lib/R/library"
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R6 "/master/rault/anaconda3/envs/TB/lib/R/library"
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ranger "/master/rault/anaconda3/envs/TB/lib/R/library"
rbokeh "/master/rault/anaconda3/envs/TB/lib/R/library"
RColorBrewer "/master/rault/anaconda3/envs/TB/lib/R/library"
Rcpp "/master/rault/anaconda3/envs/TB/lib/R/library"
RcppEigen "/master/rault/anaconda3/envs/TB/lib/R/library"
RcppRoll "/master/rault/anaconda3/envs/TB/lib/R/library"
RCurl "/master/rault/anaconda3/envs/TB/lib/R/library"
readbitmap "/master/rault/anaconda3/envs/TB/lib/R/library"
readr "/master/rault/anaconda3/envs/TB/lib/R/library"
readxl "/master/rault/anaconda3/envs/TB/lib/R/library"
recipes "/master/rault/anaconda3/envs/TB/lib/R/library"
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reshape2 "/master/rault/anaconda3/envs/TB/lib/R/library"
reticulate "/master/rault/anaconda3/envs/TB/lib/R/library"
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numDeriv "R (>= 2.11.1)"
openssl NA
org.Hs.eg.db "R (>= 2.7.0), methods, AnnotationDbi (>= 1.39.3)"
parallel NA
pbdZMQ "R (>= 3.2.0)"
pcaMethods "Biobase, methods"
pheatmap "R (>= 2.0)"
pillar NA
pkgconfig NA
plogr NA
plotly "R (>= 3.2.0), ggplot2 (>= 2.2.1)"
plyr "R (>= 3.1.0)"
png "R (>= 2.9.0)"
polynom NA
pracma "R (>= 3.1.0)"
praise NA
preprocessCore NA
prettydoc NA
prettyunits NA
pROC "R (>= 2.14)"
processx NA
prodlim "R (>= 2.9.0)"
progress NA
proxy "R (>= 3.3.2)"
psych "R (>= 2.10)"
purrr "R (>= 3.1)"
quadprog "R (>= 2.15.0)"
quantmod "R (>= 3.2.0), xts(>= 0.9-0), zoo, TTR(>= 0.2), methods"
R.methodsS3 "R (>= 2.13.0)"
R.oo "R (>= 2.13.0), R.methodsS3 (>= 1.7.1)"
R.utils "R (>= 2.14.0), R.oo (>= 1.21.0)"
R6 "R (>= 3.0)"
randomForest "R (>= 2.5.0), stats"
ranger "R (>= 3.1)"
rbokeh NA
RColorBrewer "R (>= 2.0.0)"
Rcpp "R (>= 3.0.0)"
RcppEigen "R (>= 2.15.1)"
RcppRoll "R (>= 2.15.1)"
RCurl "R (>= 3.0.0), methods, bitops"
readbitmap NA
readr "R (>= 3.0.2)"
readxl NA
recipes "R (>= 3.1), dplyr, broom"
rematch NA
repr "R (>= 3.0.1)"
reprex "R (>= 3.0.2)"
reshape "R (>= 2.6.1)"
reshape2 "R (>= 3.1)"
reticulate "R (>= 3.0)"
RevoIOQ "R (>= 2.6.2), RUnit (>= 0.4.26), tools"
RevoMods "R (>= 2.6.2)"
RevoUtils NA
RevoUtilsMath NA
rgexf "XML, Rook, igraph"
rhdf5 "methods"
rlang "R (>= 3.1.0)"
rmarkdown "R (>= 3.0)"
rmeta "grid"
Rmisc "lattice, plyr"
RMySQL "R (>= 2.8.0), DBI (>= 0.4)"
robustbase "R (>= 3.0.2)"
ROCR "gplots, methods"
Rook "R (>= 2.13.0)"
rpart "R (>= 2.15.0), graphics, stats, grDevices"
rprojroot "R (>= 3.0.0)"
RSQLite "R (>= 3.1.0)"
rstudioapi NA
Rttf2pt1 "R (>= 2.15)"
RUnit "R (>= 2.5.0), utils (>= 2.5.0), methods (>= 2.5.0)"
rvcheck "R (>= 3.3.0)"
rvest "R (>= 3.0.1), xml2"
S4Vectors "R (>= 3.3.0), methods, utils, stats, stats4, BiocGenerics (>=\n0.23.3)"
scales "R (>= 2.13)"
selectr "R (>= 2.15.2)"
sfsmisc "R (>= 3.0.1)"
shiny "R (>= 3.0.2), methods"
snow "R (>= 2.13.1), utils"
snplist "R (>= 3.0.0), RSQLite (>= 1.1)"
sourcetools "R (>= 3.0.2)"
sp "R (>= 3.0.0), methods"
spam "R (>= 3.1), dotCall64, grid, methods"
spatial "R (>= 3.0.0), graphics, stats, utils"
splines NA
splus2R "R (>= 2.7.2)"
stats NA
stats4 NA
stringi "R (>= 2.14)"
stringr "R (>= 2.14)"
survival "R (>= 2.13.0)"
sva "R (>= 3.2), mgcv, genefilter, BiocParallel"
tcltk NA
tensorflow "R (>= 3.1)"
testthat "R (>= 3.1)"
tfruns "R (>= 3.1)"
tibble "R (>= 3.1.0)"
tidyr "R (>= 3.1.0)"
tidyselect "R (>= 3.1.0)"
tidyverse NA
timeDate "R (>= 2.15.1), graphics, utils, stats, methods"
tools NA
TTR NA
utf8 "R (>= 2.10)"
utils NA
uuid "R (>= 2.9.0)"
vcd "R (>= 2.4.0), grid"
verification "R (>= 2.10), methods, fields, boot, CircStats, MASS, dtw"
viridis "R (>= 2.10), viridisLite (>= 0.2.0)"
viridisLite "R (>= 2.10)"
visNetwork "R (>= 3.0)"
whisker NA
withr "R (>= 3.0.2)"
xgboost "R (>= 3.3.0)"
XML "R (>= 2.13.0), methods, utils"
xml2 "R (>= 3.1.0)"
xtable "R (>= 2.10.0)"
xts "zoo (>= 1.7-12)"
yaml NA
zeallot NA
zlibbioc NA
zoo "R (>= 2.10.0), stats"
Imports
acepack NA
akima "sp"
annotate "Biobase, DBI, xtable, graphics, utils, stats, methods,\nBiocGenerics (>= 0.13.8), RCurl"
AnnotationDbi "methods, utils, DBI, RSQLite, stats4, BiocGenerics, Biobase,\nS4Vectors (>= 0.9.25), IRanges"
assertthat "tools"
backports "utils"
base NA
base64 "openssl"
base64enc NA
BH NA
bindr NA
bindrcpp "Rcpp, bindr"
Biobase "methods"
BiocGenerics "methods, utils, graphics, stats, parallel"
BiocInstaller NA
BiocParallel "stats, utils, futile.logger, parallel, snow"
biomaRt "utils, XML, RCurl, AnnotationDbi, progress, stringr, httr"
bit NA
bit64 NA
bitops NA
blob "methods, prettyunits"
bmp NA
boot NA
brew NA
broom "plyr, dplyr, tidyr, psych, stringr, reshape2, nlme, methods"
Cairo "grDevices, graphics"
callr NA
caret "foreach, methods, plyr, ModelMetrics (>= 1.1.0), nlme,\nreshape2, stats, stats4, utils, grDevices, recipes (>= 0.0.1),\nwithr (>= 2.0.0)"
caTools "bitops"
cellranger "rematch, tibble"
checkmate "backports (>= 1.1.0), utils"
checkpoint "\nutils"
CircStats NA
class "MASS"
cli "assertthat, crayon, methods"
clipr "utils"
cluster "graphics, grDevices, stats, utils"
COCONUT NA
codetools NA
colorspace "graphics, grDevices"
compiler NA
config "yaml (>= 2.1.13)"
cowplot "grid (>= 3.0.0), gtable (>= 0.1.2), plyr (>= 1.8.2),\ngrDevices, methods, scales, utils"
crayon "grDevices, methods, utils"
crosstalk "htmltools (>= 0.3.5), jsonlite, lazyeval, R6, shiny (>= 0.11),\nggplot2"
curl NA
CVST NA
data.table "methods"
datasets NA
DBI NA
dbplyr "assertthat, DBI (>= 0.5), dplyr (>= 0.5.0.9004), glue,\nmethods, purrr, rlang (>= 0.1.0), tibble (>= 1.3.0.9007), R6,\nutils"
ddalpha "Rcpp (>= 0.11.0)"
debugme "crayon, grDevices"
DeconRNASeq NA
deepnet NA
DEoptimR "stats"
deployrRserve NA
DiagrammeR "dplyr (>= 0.7.2), downloader (>= 0.4), htmltools (>= 0.3.6),\nhtmlwidgets (>= 0.9), igraph (>= 1.1.2), influenceR (>= 0.1.0),\nmagrittr (>= 1.5), purrr (>= 0.2.3), RColorBrewer (>= 1.1-2),\nreadr (>= 1.1.1), rlang (>= 0.1.1), rstudioapi (>= 0.6), rgexf\n(>= 0.15.3), scales (>= 0.5.0), stringr (>= 1.2.0), tibble (>=\n1.3.3), tidyr (>= 0.6.3), viridis (>= 0.4.0), visNetwork (>=\n2.0.1)"
dichromat NA
digest NA
dimRed NA
doParallel NA
dotCall64 NA
downloader "utils, digest"
dplyr "assertthat, bindrcpp (>= 0.2), glue (>= 1.1.1), magrittr,\nmethods, pkgconfig, rlang (>= 0.1.2), R6, Rcpp (>= 0.12.7),\ntibble (>= 1.3.1), utils"
DRR "stats, methods"
DT "htmltools (>= 0.3.5), htmlwidgets (>= 0.6), magrittr"
dtangle NA
dtw "graphics, grDevices, stats, utils"
e1071 "graphics, grDevices, class, stats, methods, utils"
EpiDISH "MASS, e1071, quadprog"
evaluate "methods, stringr (>= 0.6.2)"
extrafontdb NA
fdrtool "graphics, grDevices, stats"
fields NA
FNN NA
fontBitstreamVera NA
fontLiberation NA
fontquiver "fontBitstreamVera (>= 0.1.0), fontLiberation (>= 0.1.0)"
forcats "tibble, magrittr"
foreach "codetools, utils, iterators"
foreign "methods, utils, stats"
formatR NA
Formula NA
futile.logger "utils, lambda.r (>= 1.1.0), futile.options"
futile.options NA
gbm NA
gdata "gtools, stats, methods, utils"
genefilter "S4Vectors (>= 0.9.42), AnnotationDbi, annotate, Biobase,\ngraphics, methods, stats, survival"
GEOmetadb NA
GEOquery "httr, readr, xml2, dplyr, tidyr, magrittr, limma"
GGally "ggplot2 (>= 2.2.0), grDevices, grid, gtable (>= 0.2.0), plyr\n(>= 1.8.3), progress, RColorBrewer, reshape (>= 0.8.5), utils"
ggplot2 "digest, grid, gtable (>= 0.1.1), MASS, plyr (>= 1.7.1),\nreshape2, scales (>= 0.4.1), stats, tibble, lazyeval"
ggplotify "ggplot2, graphics, grDevices, grid, gridGraphics, rvcheck"
ggpmisc "methods, tibble (>= 1.3.4), MASS (>= 7.3-47), polynom (>=\n1.3-9), splus2R (>= 1.2-2), plyr (>= 1.8.4), dplyr (>= 0.7.3),\nxts (>= 0.10-0), zoo (>= 1.8-0), broom (>= 0.4.2), lubridate\n(>= 1.6.0)"
ggpubr "ggrepel, grid, ggsci, stats, utils, tidyr, purrr, dplyr(>=\n0.7.1), cowplot, ggsignif, scales, gridExtra"
ggrepel "grid, Rcpp, scales (>= 0.3.0)"
ggsci "grDevices, scales, ggplot2 (>= 2.0.0)"
ggsignif "ggplot2 (>= 2.0.0)"
glmnet "methods"
glue "methods"
gower NA
gplots "gtools, gdata, stats, caTools, KernSmooth"
graphics "grDevices"
grDevices NA
grid "grDevices, utils"
gridExtra "gtable, grid, grDevices, graphics, utils"
gridGraphics "grDevices"
gtable "grid"
gtools NA
haven "Rcpp (>= 0.11.4), readr (>= 0.1.0), hms, tibble, forcats (>=\n0.2.0)"
hexbin "lattice, grid, graphics, grDevices, stats, utils"
HGNChelper NA
highr NA
Hmisc "methods, latticeExtra, cluster, rpart, nnet, acepack, foreign,\ngtable, grid, gridExtra, data.table, htmlTable, viridis,\nhtmltools, base64enc"
hms "methods, pkgconfig, rlang"
htmlTable "stringr, knitr (>= 1.6), magrittr (>= 1.5), methods,\ncheckmate, htmlwidgets, htmltools, rstudioapi (>= 0.6)"
htmltools "utils, digest, Rcpp"
htmlwidgets "htmltools (>= 0.3), jsonlite (>= 0.9.16), yaml"
httpuv "Rcpp (>= 0.11.0), utils"
httr "jsonlite, mime, curl (>= 0.9.1), openssl (>= 0.8), R6"
HybridMTest "stats"
igraph "graphics, grDevices, irlba, magrittr, Matrix, pkgconfig (>=\n2.0.0), stats, utils"
IlluminaDataTestFiles NA
illuminaHumanv4.db "methods, AnnotationDbi"
illuminaio "base64"
influenceR "igraph (>= 1.0.1), Matrix (>= 1.1-4), methods, utils"
ipred "rpart (>= 3.1-8), MASS, survival, nnet, class, prodlim"
IRanges "stats4"
IRdisplay "repr"
IRkernel "repr (>= 0.4.99),\nmethods,\nevaluate (>= 0.10),\nIRdisplay (>= 0.3.0.9999),\npbdZMQ (>= 0.2-1),\ncrayon,\njsonlite (>= 0.9.6),\nuuid,\ndigest"
irlba "stats, methods"
iterators NA
jpeg NA
jsonlite NA
keras "reticulate (>= 1.3.1), tensorflow (>= 1.4.1), tfruns (>= 1.0),\nmagrittr, zeallot, methods, R6"
kernlab "methods, stats, grDevices, graphics"
KernSmooth NA
knitr "evaluate (>= 0.10), digest, highr, markdown, stringr (>= 0.6),\nyaml, methods, tools"
ks "FNN (>= 1.1), kernlab, KernSmooth (>= 2.22), Matrix, mclust,\nmgcv, multicool, mvtnorm (>= 1.0-0)"
labeling NA
lambda.r "formatR"
lattice "grid, grDevices, graphics, stats, utils"
latticeExtra "grid, stats, utils, grDevices"
lava "grDevices, graphics, methods, numDeriv, stats, survival, utils"
lazyeval NA
limma "grDevices, graphics, stats, utils, methods"
limSolve "quadprog, lpSolve, MASS"
lme4 "graphics, grid, splines, utils, parallel, MASS, lattice, nlme\n(>= 3.1-123), minqa (>= 1.1.15), nloptr (>= 1.0.4)"
lmerTest "plyr, MASS, Hmisc, ggplot2"
lmtest "graphics"
logcondens "ks, graphics, stats"
lpSolve NA
lubridate "stringr, Rcpp (>= 0.11),"
magrittr NA
manhattanly "stats, magrittr (>= 1.0.1), plotly (>= 4.5.6), ggplot2 (>=\n2.1.0)"
maps "graphics, utils"
markdown "utils, mime (>= 0.3)"
MASS "methods"
Matrix "methods, graphics, grid, stats, utils, lattice"
matrixStats NA
mclust "stats, utils, graphics, grDevices"
memoise "digest (>= 0.6.3)"
MetaIntegrator "rmeta, multtest, ggplot2, parallel, Rmisc, gplots, Biobase,\nRMySQL, DBI, stringr, preprocessCore, GEOquery, GEOmetadb,\nRSQLite, data.table, ggpubr, ROCR, zoo, pracma, COCONUT,\nMetrics, manhattanly, snplist, DT, pheatmap, plyr, boot, dplyr,\nreshape2, rmarkdown, AnnotationDbi, HGNChelper, magrittr, readr"
methods "utils, stats"
Metrics NA
mgcv "methods, stats, graphics, Matrix"
microbenchmark "graphics, stats"
MicrosoftR NA
mime "tools"
minqa "Rcpp (>= 0.9.10)"
mlbench NA
mnormt NA
ModelMetrics "Rcpp"
modelr "magrittr, purrr (>= 0.2.2), lazyeval (>= 0.2.0), tibble,\nbroom, dplyr, tidyr (>= 0.6.0)"
multicool NA
multtest "survival, MASS, stats4"
munsell "colorspace, methods"
mvtnorm "stats, methods"
nlme "graphics, stats, utils, lattice"
nloptr NA
nnet NA
numDeriv NA
openssl NA
org.Hs.eg.db "methods, AnnotationDbi"
parallel "tools, compiler"
pbdZMQ "R6"
pcaMethods "BiocGenerics, Rcpp (>= 0.11.3), MASS"
pheatmap "grid, RColorBrewer, scales, gtable, stats, grDevices, graphics"
pillar "cli, crayon (>= 1.3.4), methods, rlang, utf8"
pkgconfig "utils"
plogr NA
plotly "tools, scales, httr, jsonlite, magrittr, digest, viridisLite,\nbase64enc, htmltools, htmlwidgets (>= 0.9), tidyr, hexbin,\nRColorBrewer, dplyr, tibble, lazyeval (>= 0.2.0), crosstalk,\npurrr, data.table"
plyr "Rcpp (>= 0.11.0)"
png NA
polynom "stats, graphics"
pracma "graphics, grDevices, stats, utils"
praise NA
preprocessCore "stats"
prettydoc "rmarkdown (>= 1.0)"
prettyunits "magrittr, assertthat, methods"
pROC "plyr, utils, methods, Rcpp (>= 0.11.1), ggplot2,"
processx "assertthat, crayon, debugme, R6, utils"
prodlim "Rcpp (>= 0.11.5), stats, graphics, survival, KernSmooth, lava"
progress "prettyunits, R6"
proxy "stats, utils"
psych "mnormt,parallel,stats,graphics,grDevices,methods,foreign,lattice,nlme"
purrr "magrittr (>= 1.5), rlang (>= 0.1), tibble"
quadprog NA
quantmod "curl"
R.methodsS3 "utils"
R.oo "methods, utils"
R.utils "methods, utils, tools, R.methodsS3 (>= 1.7.1)"
R6 NA
randomForest NA
ranger "Rcpp (>= 0.11.2), Matrix"
rbokeh "R6,\njsonlite,\ndigest,\nmethods,\nhtmlwidgets,\nmagrittr,\ncurl,\nglue,\nrlang,\nscales,\nhexbin,\nlattice,\nmaps"
RColorBrewer NA
Rcpp "methods, utils"
RcppEigen "Matrix (>= 1.1-0), Rcpp (>= 0.11.0), stats, utils"
RcppRoll "Rcpp"
RCurl NA
readbitmap "bmp, jpeg, png"
readr "Rcpp (>= 0.12.0.5), tibble, hms, R6"
readxl "cellranger, Rcpp (>= 0.11.6), tibble (>= 1.1)"
recipes "tibble, stats, ipred, dimRed (>= 0.1.0), lubridate, timeDate,\nddalpha, purrr, rlang (>= 0.1.1), gower, RcppRoll, tidyselect\n(>= 0.1.1), magrittr, Matrix"
rematch NA
repr "utils, grDevices"
reprex "callr, clipr (>= 0.3.0), knitr, rmarkdown, tools, utils,\nwhisker"
reshape "plyr"
reshape2 "plyr (>= 1.8.1), Rcpp, stringr"
reticulate "utils, graphics, jsonlite, Rcpp (>= 0.12.7)"
RevoIOQ NA
RevoMods NA
RevoUtils NA
RevoUtilsMath NA
rgexf NA
rhdf5 "zlibbioc"
rlang NA
rmarkdown "tools, utils, knitr (>= 1.14), yaml (>= 2.1.5), htmltools (>=\n0.3.5), evaluate (>= 0.8), base64enc, jsonlite, rprojroot,\nmime, methods, stringr (>= 1.2.0)"
rmeta NA
Rmisc NA
RMySQL "methods"
robustbase "stats, graphics, utils, methods, DEoptimR"
ROCR NA
Rook "utils, tools, methods, brew"
rpart NA
rprojroot "backports"
RSQLite "bit64, blob (>= 1.1.1), DBI (>= 0.8), memoise, methods,\npkgconfig, Rcpp (>= 0.12.7)"
rstudioapi NA
Rttf2pt1 NA
RUnit NA
rvcheck "utils"
rvest "httr (>= 0.5), selectr, magrittr"
S4Vectors NA
scales "RColorBrewer, dichromat, plyr, munsell (>= 0.2), labeling,\nRcpp, R6, viridisLite"
selectr "methods, stringr"
sfsmisc "grDevices, methods, utils, stats"
shiny "utils, httpuv (>= 1.3.5), mime (>= 0.3), jsonlite (>= 0.9.16),\nxtable, digest, htmltools (>= 0.3.5), R6 (>= 2.0), sourcetools,\ntools"
snow NA
snplist "biomaRt (>= 2.16.0), Rcpp (>= 0.10.5), R.utils (>= 1.27.1),\nDBI (>= 0.3.1)"
sourcetools NA
sp "utils, stats, graphics, grDevices, lattice, grid"
spam NA
spatial NA
splines "graphics, stats"
splus2R "methods"
stats "utils, grDevices, graphics"
stats4 "graphics, methods, stats"
stringi "tools, utils, stats"
stringr "stringi (>= 0.4.1), magrittr"
survival "graphics, Matrix, methods, splines, stats, utils"
sva "matrixStats, stats, graphics, utils, limma,"
tcltk "utils"
tensorflow "config, jsonlite (>= 1.2), processx, reticulate (>= 1.3),\ntfruns (>= 1.0), utils, yaml, rstudioapi (>= 0.7)"
testthat "cli, crayon, digest, magrittr, methods, praise, R6 (>= 2.2.0),\nrlang, withr (>= 2.0.0)"
tfruns "utils, jsonlite (>= 1.2), base64enc, yaml, config, magrittr,\ntibble, whisker, tidyselect, rlang, rstudioapi (>= 0.7),\nreticulate"
tibble "crayon, methods, pillar, rlang, utils"
tidyr "dplyr (>= 0.7.0), glue, magrittr, purrr, rlang, Rcpp, stringi,\ntibble, tidyselect"
tidyselect "glue, purrr, rlang (>= 0.1), Rcpp (>= 0.12.0)"
tidyverse "broom (>= 0.4.2), cli (>= 1.0.0), crayon (>= 1.3.4), dplyr (>=\n0.7.4), dbplyr (>= 1.1.0), forcats (>= 0.2.0), ggplot2 (>=\n2.2.1), haven (>= 1.1.0), hms (>= 0.3), httr (>= 1.3.1),\njsonlite (>= 1.5), lubridate (>= 1.7.1), magrittr (>= 1.5),\nmodelr (>= 0.1.1), purrr (>= 0.2.4), readr (>= 1.1.1), readxl\n(>= 1.0.0), reprex (>= 0.1.1), rlang (>= 0.1.4), rstudioapi (>=\n0.7), rvest (>= 0.3.2), stringr (>= 1.2.0), tibble (>= 1.3.4),\ntidyr (>= 0.7.2), xml2 (>= 1.1.1)"
timeDate NA
tools NA
TTR "xts (>= 0.10-0), zoo, curl"
utf8 NA
utils NA
uuid NA
vcd "stats, utils, MASS, grDevices, colorspace, lmtest"
verification "graphics, stats"
viridis "stats, ggplot2 (>= 1.0.1), gridExtra"
viridisLite NA
visNetwork "htmlwidgets, htmltools, jsonlite, magrittr, utils, methods,\ngrDevices, stats"
whisker NA
withr "stats, graphics, grDevices"
xgboost "Matrix (>= 1.1-0), methods, data.table (>= 1.9.6), magrittr\n(>= 1.5), stringi (>= 0.5.2)"
XML NA
xml2 "Rcpp"
xtable "stats, utils"
xts "methods"
yaml NA
zeallot NA
zlibbioc NA
zoo "utils, graphics, grDevices, lattice (>= 0.20-27)"
LinkingTo
acepack NA
akima NA
annotate NA
AnnotationDbi NA
assertthat NA
backports NA
base NA
base64 NA
base64enc NA
BH NA
bindr NA
bindrcpp "Rcpp, plogr"
Biobase NA
BiocGenerics NA
BiocInstaller NA
BiocParallel "BH"
biomaRt NA
bit NA
bit64 NA
bitops NA
blob NA
bmp NA
boot NA
brew NA
broom NA
Cairo NA
callr NA
caret NA
caTools NA
cellranger NA
checkmate NA
checkpoint NA
CircStats NA
class NA
cli NA
clipr NA
cluster NA
COCONUT NA
codetools NA
colorspace NA
compiler NA
config NA
cowplot NA
crayon NA
crosstalk NA
curl NA
CVST NA
data.table NA
datasets NA
DBI NA
dbplyr NA
ddalpha "BH, Rcpp"
debugme NA
DeconRNASeq NA
deepnet NA
DEoptimR NA
deployrRserve NA
DiagrammeR NA
dichromat NA
digest NA
dimRed NA
doParallel NA
dotCall64 NA
downloader NA
dplyr "Rcpp (>= 0.12.0), BH (>= 1.58.0-1), bindrcpp, plogr"
DRR NA
DT NA
dtangle NA
dtw NA
e1071 NA
EpiDISH NA
evaluate NA
extrafontdb NA
fdrtool NA
fields NA
FNN NA
fontBitstreamVera NA
fontLiberation NA
fontquiver NA
forcats NA
foreach NA
foreign NA
formatR NA
Formula NA
futile.logger NA
futile.options NA
gbm NA
gdata NA
genefilter NA
GEOmetadb NA
GEOquery NA
GGally NA
ggplot2 NA
ggplotify NA
ggpmisc NA
ggpubr NA
ggrepel "Rcpp"
ggsci NA
ggsignif NA
glmnet NA
glue NA
gower NA
gplots NA
graphics NA
grDevices NA
grid NA
gridExtra NA
gridGraphics NA
gtable NA
gtools NA
haven "Rcpp"
hexbin NA
HGNChelper NA
highr NA
Hmisc NA
hms NA
htmlTable NA
htmltools "Rcpp"
htmlwidgets NA
httpuv "Rcpp"
httr NA
HybridMTest NA
igraph NA
IlluminaDataTestFiles NA
illuminaHumanv4.db NA
illuminaio NA
influenceR NA
ipred NA
IRanges "S4Vectors"
IRdisplay NA
IRkernel NA
irlba "Matrix"
iterators NA
jpeg NA
jsonlite NA
keras NA
kernlab NA
KernSmooth NA
knitr NA
ks NA
labeling NA
lambda.r NA
lattice NA
latticeExtra NA
lava NA
lazyeval NA
limma NA
limSolve NA
lme4 "Rcpp (>= 0.10.5), RcppEigen"
lmerTest NA
lmtest NA
logcondens NA
lpSolve NA
lubridate "Rcpp,"
magrittr NA
manhattanly NA
maps NA
markdown NA
MASS NA
Matrix NA
matrixStats NA
mclust NA
memoise NA
MetaIntegrator NA
methods NA
Metrics NA
mgcv NA
microbenchmark NA
MicrosoftR NA
mime NA
minqa "Rcpp"
mlbench NA
mnormt NA
ModelMetrics "Rcpp"
modelr NA
multicool "Rcpp"
multtest NA
munsell NA
mvtnorm NA
nlme NA
nloptr NA
nnet NA
numDeriv NA
openssl NA
org.Hs.eg.db NA
parallel NA
pbdZMQ NA
pcaMethods "Rcpp"
pheatmap NA
pillar NA
pkgconfig NA
plogr NA
plotly NA
plyr "Rcpp"
png NA
polynom NA
pracma NA
praise NA
preprocessCore NA
prettydoc NA
prettyunits NA
pROC "Rcpp"
processx NA
prodlim "Rcpp"
progress NA
proxy NA
psych NA
purrr NA
quadprog NA
quantmod NA
R.methodsS3 NA
R.oo NA
R.utils NA
R6 NA
randomForest NA
ranger "Rcpp, RcppEigen"
rbokeh NA
RColorBrewer NA
Rcpp NA
RcppEigen "Rcpp"
RcppRoll "Rcpp"
RCurl NA
readbitmap NA
readr "Rcpp, BH"
readxl "Rcpp"
recipes NA
rematch NA
repr NA
reprex NA
reshape NA
reshape2 "Rcpp"
reticulate "Rcpp"
RevoIOQ NA
RevoMods NA
RevoUtils NA
RevoUtilsMath NA
rgexf NA
rhdf5 NA
rlang NA
rmarkdown NA
rmeta NA
Rmisc NA
RMySQL NA
robustbase NA
ROCR NA
Rook NA
rpart NA
rprojroot NA
RSQLite "BH, plogr (>= 0.2.0), Rcpp"
rstudioapi NA
Rttf2pt1 NA
RUnit NA
rvcheck NA
rvest NA
S4Vectors NA
scales "Rcpp"
selectr NA
sfsmisc NA
shiny NA
snow NA
snplist "Rcpp"
sourcetools NA
sp NA
spam NA
spatial NA
splines NA
splus2R NA
stats NA
stats4 NA
stringi NA
stringr NA
survival NA
sva NA
tcltk NA
tensorflow NA
testthat NA
tfruns NA
tibble NA
tidyr "Rcpp"
tidyselect "Rcpp (>= 0.12.0),"
tidyverse NA
timeDate NA
tools NA
TTR "xts"
utf8 NA
utils NA
uuid NA
vcd NA
verification NA
viridis NA
viridisLite NA
visNetwork NA
whisker NA
withr NA
xgboost NA
XML NA
xml2 "Rcpp (>= 0.11.4.6), BH"
xtable NA
xts "zoo"
yaml NA
zeallot NA
zlibbioc NA
zoo NA
Suggests
acepack "testthat"
akima NA
annotate "hgu95av2.db, genefilter, Biostrings (>= 2.25.10), IRanges,\nrae230a.db, rae230aprobe, tkWidgets, GO.db, org.Hs.eg.db,\norg.Mm.eg.db, hom.Hs.inp.db, humanCHRLOC, Rgraphviz, RUnit,"
AnnotationDbi "DBI (>= 0.2-4), RSQLite (>= 0.6-4), hgu95av2.db, GO.db,\norg.Sc.sgd.db, org.At.tair.db, KEGG.db, RUnit,\nTxDb.Hsapiens.UCSC.hg19.knownGene, hom.Hs.inp.db, org.Hs.eg.db,\nreactome.db, AnnotationForge, graph, EnsDb.Hsapiens.v75,\nBiocStyle, knitr"
assertthat "testthat"
backports NA
base "methods"
base64 NA
base64enc NA
BH NA
bindr "testthat"
bindrcpp "testthat"
Biobase "tools, tkWidgets, ALL, RUnit, golubEsets"
BiocGenerics "Biobase, S4Vectors, IRanges, GenomicRanges, AnnotationDbi,\noligoClasses, oligo, affyPLM, flowClust, affy, DESeq2, MSnbase,\nannotate, RUnit"
BiocInstaller "devtools, RUnit, BiocGenerics"
BiocParallel "BiocGenerics, tools, foreach, BatchJobs, BBmisc, doParallel,\nRmpi, GenomicRanges, RNAseqData.HNRNPC.bam.chr14,\nTxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation,\nRsamtools, GenomicAlignments, ShortRead, codetools, RUnit,\nBiocStyle, knitr"
biomaRt "annotate, BiocStyle, knitr, rmarkdown, testthat"
bit NA
bit64 NA
bitops NA
blob "covr, pillar (>= 1.2.1), testthat"
bmp "pixmap, testthat"
boot "MASS, survival"
brew NA
broom "knitr, boot, survival, gam, glmnet, lfe, Lahman, MASS, sp,\nmaps, maptools, multcomp, testthat, lme4, zoo, lmtest, plm,\nbiglm, ggplot2, nnet, geepack, AUC, ergm, network,\nstatnet.common, xergm, btergm, binGroup, Hmisc, bbmle, gamlss,\nrstan, rstanarm, brms, coda, gmm, Matrix, ks, purrr, orcutt,\nmgcv, lmodel2, poLCA, mclust, covr, lsmeans, emmeans, betareg,\nrobust, akima, AER, muhaz, speedglm, tibble"
Cairo "png"
callr "covr, testthat"
caret "BradleyTerry2, e1071, earth (>= 2.2-3), fastICA, gam, ipred,\nkernlab, klaR, MASS, ellipse, mda, mgcv, mlbench, MLmetrics,\nnnet, party (>= 0.9-99992), pls, pROC, proxy, randomForest,\nRANN, spls, subselect, pamr, superpc, Cubist, testthat (>=\n0.9.1)"
caTools "MASS, rpart"
cellranger "covr, testthat (>= 1.0.0), knitr, rmarkdown"
checkmate "R6, bit, fastmatch, data.table (>= 1.9.8), devtools, ggplot2,\nknitr, magrittr, microbenchmark, rmarkdown, testthat (>=\n0.11.0), tibble"
checkpoint "\nknitr,\nrmarkdown,\ntestthat(>= 0.9),\nMASS,\ndarts,\nmockery,\ncli"
CircStats NA
class NA
cli "covr, mockery, testthat, withr"
clipr "rstudioapi (>= 0.5), testthat"
cluster "MASS"
COCONUT "limma, parallel"
codetools NA
colorspace "datasets, stats, utils, KernSmooth, MASS, kernlab, mvtnorm,\nvcd, dichromat, tcltk, shiny, shinyjs"
compiler NA
config "testthat, knitr"
cowplot "covr, gridGraphics, knitr, magick, maps, dplyr, tidyr,\ntestthat, vdiffr, viridis"
crayon "mockery, rstudioapi, testthat, withr"
crosstalk NA
curl "\ntestthat (>= 1.0.0),\nknitr,\njsonlite,\nrmarkdown,\nmagrittr,\nhttpuv,\nwebutils"
CVST NA
data.table "bit64, knitr, nanotime, chron, ggplot2 (>= 0.9.0), plyr,\nreshape, reshape2, testthat (>= 0.4), hexbin, fastmatch, nlme,\nxts, gdata, GenomicRanges, caret, curl, zoo, plm, rmarkdown,\nparallel"
datasets NA
DBI "blob, covr, hms, knitr, magrittr, rprojroot, rmarkdown,\nRSQLite (>= 1.1-2), testthat, xml2"
dbplyr "covr, knitr, Lahman (>= 3.0-1), nycflights13, rmarkdown,\nRSQLite (>= 1.0.0), RMySQL, RPostgreSQL, testthat"
ddalpha NA
debugme "covr, mockery, R6, testthat, withr"
DeconRNASeq NA
deepnet NA
DEoptimR NA
deployrRserve NA
DiagrammeR "covr, DiagrammeRsvg, rsvg, knitr, testthat"
dichromat NA
digest "knitr, rmarkdown"
dimRed "MASS, Matrix, RANN, RSpectra, Rtsne, coRanking, diffusionMap,\nenergy, fastICA, ggplot2, graphics, igraph, kernlab, lle, loe,\noptimx, pcaPP, rgl, scales, scatterplot3d, stats, testthat,\ntidyr, vegan"
doParallel "caret, mlbench, rpart"
dotCall64 "microbenchmark, OpenMPController, RColorBrewer, roxygen2,\nspam, testthat,"
downloader "testthat"
dplyr "bit64, covr, dbplyr, dtplyr, DBI, ggplot2, hms, knitr, Lahman\n(>= 3.0-1), mgcv, microbenchmark, nycflights13, rmarkdown,\nRMySQL, RPostgreSQL, RSQLite, testthat, withr"
DRR "knitr"
DT "jsonlite (>= 0.9.16), knitr (>= 1.8), rmarkdown, shiny (>=\n0.12.1)"
dtangle "knitr, rmarkdown, testthat"
dtw NA
e1071 "cluster, mlbench, nnet, randomForest, rpart, SparseM, xtable,\nMatrix, MASS"
EpiDISH "roxygen2, GEOquery, BiocStyle, knitr, rmarkdown, Biobase,\ntestthat"
evaluate "testthat, lattice, ggplot2"
extrafontdb NA
fdrtool ""
fields NA
FNN "chemometrics, mvtnorm"
fontBitstreamVera NA
fontLiberation NA
fontquiver "testthat, htmltools"
forcats "ggplot2, testthat, covr"
foreach "randomForest"
foreign NA
formatR "codetools, shiny, testit, rmarkdown, knitr"
Formula NA
futile.logger "testthat, jsonlite"
futile.options NA
gbm "RUnit"
gdata "RUnit"
genefilter "class, hgu95av2.db, tkWidgets, ALL, ROC, DESeq, pasilla,\nBiocStyle, knitr"
GEOmetadb "knitr, rmarkdown, dplyr, tm, wordcloud"
GEOquery "knitr, rmarkdown, BiocGenerics, testthat, covr"
GGally "broom (>= 0.4.0), chemometrics, geosphere (>= 1.5-1), igraph\n(>= 1.0.1), intergraph (>= 2.0-2), maps (>= 3.1.0), mapproj,\nnetwork (>= 1.12.0), scagnostics, scales (>= 0.4.0), sna (>=\n2.3-2), survival, packagedocs (>= 0.4.0), rmarkdown, roxygen2,\ntestthat"
ggplot2 "covr, ggplot2movies, hexbin, Hmisc, lattice, mapproj, maps,\nmaptools, mgcv, multcomp, nlme, testthat (>= 0.11.0), quantreg,\nknitr, rpart, rmarkdown, svglite"
ggplotify "colorspace, cowplot, ggimage, knitr, lattice, prettydoc, vcd"
ggpmisc "knitr (>= 1.17), rmarkdown (>= 1.6), nlme (>= 3.1-131),\nggrepel (>= 0.6.5)"
ggpubr "grDevices, knitr, RColorBrewer, gtable"
ggrepel "knitr, rmarkdown, testthat"
ggsci "knitr, rmarkdown, gridExtra, reshape2"
ggsignif "testthat, knitr, rmarkdown"
glmnet "survival, knitr, lars"
glue "testthat, covr, magrittr, crayon, knitr, rmarkdown, DBI,\nRSQLite, R.utils, forcats, microbenchmark, rprintf, stringr,\nggplot2"
gower "testthat, knitr, rmarkdown"
gplots "grid, MASS"
graphics NA
grDevices "KernSmooth"
grid "lattice"
gridExtra "ggplot2, egg, lattice, knitr, testthat"
gridGraphics "magick (>= 1.3), pdftools (>= 1.6)"
gtable "testthat, covr"
gtools NA
haven "testthat, knitr, rmarkdown, covr"
hexbin "marray, affy, Biobase, limma"
HGNChelper NA
highr "knitr, testit"
Hmisc "chron, rms, mice, tables, knitr, ff, ffbase, plotly (>=\n4.5.6)"
hms "crayon, lubridate, pillar, testthat"
htmlTable "testthat, XML, xtable, ztable, Hmisc, reshape, rmarkdown,\npander, chron, lubridate, tibble, tidyr (>= 0.7.2), dplyr (>=\n0.7.4)"
htmltools "markdown, testthat"
htmlwidgets "knitr (>= 1.8)"
httpuv NA
httr "httpuv, jpeg, knitr, png, testthat (>= 0.8.0), readr, xml2,\nrmarkdown, covr"
HybridMTest NA
igraph "ape, graph, igraphdata, NMF, rgl, scales, stats4, tcltk,\ntestthat"
IlluminaDataTestFiles NA
illuminaHumanv4.db "annotate, RUnit"
illuminaio "RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2),\nBiocStyle"
influenceR "testthat"
ipred "mvtnorm, mlbench, TH.data"
IRanges "XVector, GenomicRanges, Rsamtools, GenomicAlignments,\nGenomicFeatures, BSgenome.Celegans.UCSC.ce2, pasillaBamSubset,\nRUnit"
IRdisplay "testthat, withr"
IRkernel "testthat,\nroxygen2"
irlba "PMA"
iterators "RUnit"
jpeg NA
jsonlite "httr, curl, plyr, testthat, knitr, rmarkdown, R.rsp, sp"
keras "ggplot2, testthat, knitr, rmarkdown"
kernlab NA
KernSmooth "MASS"
knitr "formatR, testit, rgl (>= 0.95.1201), codetools, rmarkdown,\nhtmlwidgets (>= 0.7), webshot, tikzDevice (>= 0.10), reticulate\n(>= 1.3.1), JuliaCall (>= 0.11.1), png, jpeg, xml2, httr, DBI\n(>= 0.4-1), tibble"
ks "maps, MASS, misc3d (>= 0.4-0), OceanView, oz, rgl (>= 0.66)"
labeling NA
lambda.r "testit"
lattice "KernSmooth, MASS, latticeExtra"
latticeExtra "maps, mapproj, deldir, tripack, zoo, MASS, quantreg, mgcv"
lava "KernSmooth, Matrix, Rgraphviz, ascii, data.table, fields,\nforeach, geepack, gof (>= 0.9), graph, igraph (>= 0.6),\nlava.tobit, lme4, mets (>= 1.1), optimx, quantreg, rgl,\ntestthat (>= 0.11), visNetwork, zoo"
lazyeval "knitr, rmarkdown (>= 0.2.65), testthat, covr"
limma "affy, AnnotationDbi, BiasedUrn, Biobase, ellipse, GO.db,\ngplots, illuminaio, locfit, MASS, org.Hs.eg.db, splines,\nstatmod (>= 1.2.2), vsn"
limSolve NA
lme4 "knitr, boot, PKPDmodels, MEMSS, testthat (>= 0.8.1), ggplot2,\nmlmRev, optimx (>= 2013.8.6), gamm4, pbkrtest, HSAUR2, numDeriv"
lmerTest "pbkrtest, nlme, estimability"
lmtest "car, strucchange, sandwich, dynlm, stats4, survival, AER"
logcondens NA
lpSolve NA
lubridate "testthat, knitr, covr"
magrittr "testthat, knitr"
manhattanly "knitr, rmarkdown"
maps "mapproj (>= 1.2-0), mapdata (>= 2.2-4), sp, maptools"
markdown "knitr, RCurl"
MASS "lattice, nlme, nnet, survival"
Matrix "expm, MASS"
matrixStats "base64enc, ggplot2, knitr, microbenchmark, R.devices, R.rsp"
mclust "knitr (>= 1.12), rmarkdown (>= 0.9), mix (>= 1.0), geometry\n(>= 0.3-6), MASS"
memoise "testthat, aws.s3, httr, covr"
MetaIntegrator "BiocStyle, knitr, RUnit, BiocGenerics"
methods "codetools"
Metrics "testthat"
mgcv "splines, parallel, survival, MASS"
microbenchmark "ggplot2, multcomp"
MicrosoftR NA
mime NA
minqa NA
mlbench "lattice"
mnormt NA
ModelMetrics "testthat"
modelr "testthat, ggplot2, covr, compiler"
multicool NA
multtest "snow"
munsell "ggplot2, testthat"
mvtnorm NA
nlme "Hmisc, MASS"
nloptr "testthat (>= 0.8.1)"
nnet "MASS"
numDeriv NA
openssl "testthat, digest, knitr, rmarkdown, jsonlite, jose"
org.Hs.eg.db "DBI, annotate, RUnit"
parallel "methods"
pbdZMQ "pbdRPC"
pcaMethods "matrixStats, lattice, ggplot2"
pheatmap NA
pillar "knitr, testthat"
pkgconfig "covr, testthat, disposables (>= 1.0.3)"
plogr "Rcpp"
plotly "MASS, maps, ggthemes, GGally, testthat, knitr, devtools,\nshiny (>= 0.14), curl, rmarkdown, Rserve, RSclient, Cairo,\nbroom, webshot, listviewer, dendextend, sf, RSelenium, png,\nIRdisplay"
plyr "abind, testthat, tcltk, foreach, doParallel, itertools,\niterators, covr"
png NA
polynom NA
pracma "quadprog"
praise "testthat"
preprocessCore NA
prettydoc "knitr, KernSmooth"
prettyunits "testthat"
pROC "microbenchmark, tcltk, MASS, logcondens, doParallel,\ntestthat, vdiffr"
processx "covr, testthat, withr"
prodlim NA
progress "testthat"
proxy "cba"
psych "GPArotation, lavaan, sem, lme4,Rcsdp, graph, Rgraphviz"
purrr "covr, dplyr (>= 0.4.3), knitr, rmarkdown, testthat"
quadprog NA
quantmod "DBI,RMySQL,RSQLite,timeSeries,XML,downloader,jsonlite(>= 1.1)"
R.methodsS3 NA
R.oo "tools"
R.utils "digest (>= 0.6.10)"
R6 "knitr, microbenchmark, pryr, testthat, ggplot2, scales"
randomForest "RColorBrewer, MASS"
ranger "survival, testthat, GenABEL"
rbokeh "testthat,\nlintr,\nroxygen2 (>= 5.0.0)"
RColorBrewer NA
Rcpp "RUnit, inline, rbenchmark, knitr, rmarkdown, pinp, pkgKitten\n(>= 0.1.2)"
RcppEigen "inline, RUnit, pkgKitten"
RcppRoll "zoo, microbenchmark, testthat, RcppArmadillo"
RCurl "Rcompression, XML"
readbitmap "pixmap, testthat"
readr "curl, testthat, knitr, rmarkdown, stringi, covr"
readxl "covr, knitr, rmarkdown, rprojroot (>= 1.1), testthat"
recipes "testthat, rpart, kernlab, fastICA, RANN, igraph, knitr,\ncaret, ggplot2, rmarkdown, covr"
rematch "covr, testthat"
repr "methods, highr, Cairo, testthat"
reprex "covr, devtools, formatR, fortunes, miniUI, rstudioapi, shiny,\nshinyjs, testthat"
reshape NA
reshape2 "covr, lattice, testthat (>= 0.8.0)"
reticulate "testthat, knitr"
RevoIOQ "RevoUtils"
RevoMods NA
RevoUtils NA
RevoUtilsMath NA
rgexf NA
rhdf5 "bit64, BiocStyle, testthat"
rlang "knitr, rmarkdown (>= 0.2.65), testthat, covr"
rmarkdown "shiny (>= 0.11), tufte, testthat, digest, tibble"
rmeta NA
Rmisc "latticeExtra, Hmisc, stats4"
RMySQL "testthat"
robustbase "grid, MASS, lattice, boot, cluster, Matrix, robust,\nfit.models, MPV, xtable, ggplot2, GGally, RColorBrewer,\nreshape2, sfsmisc, catdata"
ROCR NA
Rook NA
rpart "survival"
rprojroot "testthat, mockr, knitr, withr, rmarkdown"
RSQLite "DBItest, knitr, rmarkdown, testthat"
rstudioapi "testthat, knitr, rmarkdown"
Rttf2pt1 NA
RUnit NA
rvcheck NA
rvest "testthat, knitr, png, stringi (>= 0.3.1), rmarkdown, covr"
S4Vectors "IRanges, GenomicRanges, Matrix, ShortRead, graph, data.table,\nRUnit"
scales "testthat (>= 0.8), bit64, covr, hms"
selectr "testthat, XML, xml2"
sfsmisc "datasets, tcltk, cluster, lattice, MASS, Matrix, nlme, lokern"
shiny "datasets, Cairo (>= 1.5-5), testthat, knitr (>= 1.6),\nmarkdown, rmarkdown, ggplot2, magrittr"
snow "Rmpi,rlecuyer,nws"
snplist "knitr"
sourcetools "testthat"
sp "RColorBrewer, rgdal (>= 0.8-7), rgeos (>= 0.3-13), gstat,\nmaptools, deldir"
spam "spam64, fields, SparseM, Matrix, testthat, R.rsp, truncdist"
spatial "MASS"
splines "Matrix, methods"
splus2R NA
stats "MASS, Matrix, SuppDists, methods, stats4"
stats4 NA
stringi NA
stringr "testthat, knitr, htmltools, htmlwidgets, rmarkdown, covr"
survival NA
sva "pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat"
tcltk NA
tensorflow "testthat"
testthat "covr, devtools, knitr, rmarkdown, xml2"
tfruns "testthat, knitr"
tibble "covr, dplyr, import, knitr (>= 1.5.32), microbenchmark,\nmockr, nycflights13, testthat, rmarkdown, withr"
tidyr "knitr, testthat, covr, gapminder, rmarkdown"
tidyselect "dplyr, testthat"
tidyverse "feather (>= 0.3.1), knitr (>= 1.17), rmarkdown (>= 1.7.4)"
timeDate "date, RUnit"
tools "codetools, methods, xml2, curl"
TTR "RUnit"
utf8 "corpus, knitr, testthat"
utils "methods, XML"
uuid NA
vcd "KernSmooth, mvtnorm, kernlab, HSAUR, coin"
verification NA
viridis "hexbin (>= 1.27.0), scales, MASS, knitr, dichromat,\ncolorspace, rasterVis, httr, mapproj, vdiffr, svglite (>=\n1.2.0), testthat, covr, rmarkdown"
viridisLite "hexbin (>= 1.27.0), ggplot2 (>= 1.0.1), testthat, covr"
visNetwork "knitr, igraph, rpart, shiny, shinyWidgets, colourpicker,\nsparkline, ggraph, flashClust"
whisker "markdown"
withr "testthat, covr, lattice, DBI, RSQLite, methods, knitr,\nrmarkdown"
xgboost "knitr, rmarkdown, ggplot2 (>= 1.0.1), DiagrammeR (>= 0.9.0),\nCkmeans.1d.dp (>= 3.3.1), vcd (>= 1.3), testthat, igraph (>=\n1.0.1)"
XML "bitops, RCurl"
xml2 "testthat, curl, covr, knitr, rmarkdown, magrittr, httr"
xtable "knitr, lsmeans, spdep, splm, sphet, plm, zoo, survival"
xts "timeSeries, timeDate, tseries, chron, fts, tis, RUnit"
yaml "testthat"
zeallot "testthat, knitr, rmarkdown, purrr, magrittr"
zlibbioc NA
zoo "coda, chron, DAAG, fts, ggplot2, mondate, scales,\nstrucchange, timeDate, timeSeries, tis, tseries, xts"
Enhances
acepack NA
akima NA
annotate NA
AnnotationDbi NA
assertthat NA
backports NA
base NA
base64 NA
base64enc "png"
BH NA
bindr NA
bindrcpp NA
Biobase NA
BiocGenerics NA
BiocInstaller NA
BiocParallel NA
biomaRt NA
bit NA
bit64 NA
bitops NA
blob NA
bmp NA
boot NA
brew NA
broom NA
Cairo "FastRWeb"
callr NA
caret NA
caTools NA
cellranger NA
checkmate NA
checkpoint NA
CircStats NA
class NA
cli NA
clipr NA
cluster NA
COCONUT ""
codetools NA
colorspace NA
compiler NA
config NA
cowplot NA
crayon NA
crosstalk NA
curl NA
CVST NA
data.table NA
datasets NA
DBI NA
dbplyr NA
ddalpha NA
debugme NA
DeconRNASeq NA
deepnet NA
DEoptimR "robustbase"
deployrRserve NA
DiagrammeR NA
dichromat NA
digest NA
dimRed NA
doParallel "compiler, RUnit"
dotCall64 NA
downloader NA
dplyr NA
DRR NA
DT NA
dtangle NA
dtw NA
e1071 NA
EpiDISH NA
evaluate NA
extrafontdb NA
fdrtool NA
fields NA
FNN NA
fontBitstreamVera NA
fontLiberation NA
fontquiver NA
forcats NA
foreach "compiler, doMC, RUnit, doParallel"
foreign NA
formatR NA
Formula NA
futile.logger NA
futile.options NA
gbm NA
gdata NA
genefilter NA
GEOmetadb NA
GEOquery NA
GGally NA
ggplot2 "sp"
ggplotify NA
ggpmisc NA
ggpubr NA
ggrepel NA
ggsci NA
ggsignif NA
glmnet NA
glue NA
gower NA
gplots NA
graphics NA
grDevices NA
grid NA
gridExtra NA
gridGraphics NA
gtable NA
gtools NA
haven NA
hexbin NA
HGNChelper NA
highr NA
Hmisc NA
hms NA
htmlTable NA
htmltools "knitr"
htmlwidgets "shiny (>= 0.12)"
httpuv NA
httr NA
HybridMTest NA
igraph NA
IlluminaDataTestFiles NA
illuminaHumanv4.db NA
illuminaio NA
influenceR NA
ipred NA
IRanges NA
IRdisplay NA
IRkernel NA
irlba NA
iterators NA
jpeg NA
jsonlite NA
keras NA
kernlab NA
KernSmooth NA
knitr NA
ks NA
labeling NA
lambda.r NA
lattice "chron"
latticeExtra NA
lava NA
lazyeval NA
limma NA
limSolve NA
lme4 NA
lmerTest NA
lmtest NA
logcondens NA
lpSolve NA
lubridate "chron, fts, timeSeries, timeDate, tis, tseries, xts, zoo"
magrittr NA
manhattanly NA
maps NA
markdown NA
MASS NA
Matrix "MatrixModels, graph, SparseM, sfsmisc"
matrixStats NA
mclust NA
memoise NA
MetaIntegrator NA
methods NA
Metrics NA
mgcv NA
microbenchmark NA
MicrosoftR NA
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DEoptimR "3.4.3"
deployrRserve "3.3.2"
DiagrammeR "3.4.3"
dichromat "3.4.3"
digest "3.4.3"
dimRed "3.4.3"
doParallel "3.3.1"
dotCall64 "3.4.3"
downloader "3.4.3"
dplyr "3.4.3"
DRR "3.4.3"
DT "3.4.3"
dtangle "3.4.3"
dtw "3.4.3"
e1071 "3.4.3"
EpiDISH "3.4.3"
evaluate "3.4.3"
extrafontdb "3.4.3"
fdrtool "3.4.3"
fields "3.4.3"
FNN "3.4.3"
fontBitstreamVera "3.4.3"
fontLiberation "3.4.3"
fontquiver "3.4.3"
forcats "3.4.3"
foreach "3.3.3"
foreign "3.4.3"
formatR "3.4.3"
Formula "3.4.3"
futile.logger "3.4.3"
futile.options "3.4.3"
gbm "3.4.3"
gdata "3.4.3"
genefilter "3.4.3"
GEOmetadb "3.4.3"
GEOquery "3.4.3"
GGally "3.4.3"
ggplot2 "3.4.3"
ggplotify "3.4.3"
ggpmisc "3.4.3"
ggpubr "3.4.3"
ggrepel "3.4.3"
ggsci "3.4.3"
ggsignif "3.4.3"
glmnet "3.4.3"
glue "3.4.3"
gower "3.4.3"
gplots "3.4.3"
graphics "3.4.3"
grDevices "3.4.3"
grid "3.4.3"
gridExtra "3.4.3"
gridGraphics "3.4.3"
gtable "3.4.3"
gtools "3.4.3"
haven "3.4.3"
hexbin "3.4.3"
HGNChelper "3.4.3"
highr "3.4.3"
Hmisc "3.4.3"
hms "3.4.3"
htmlTable "3.4.3"
htmltools "3.4.3"
htmlwidgets "3.4.3"
httpuv "3.4.3"
httr "3.4.3"
HybridMTest "3.4.3"
igraph "3.4.3"
IlluminaDataTestFiles "3.4.3"
illuminaHumanv4.db "3.4.3"
illuminaio "3.4.3"
influenceR "3.4.3"
ipred "3.4.3"
IRanges "3.4.3"
IRdisplay "3.4.3"
IRkernel "3.4.3"
irlba "3.4.3"
iterators "3.3.1"
jpeg "3.4.3"
jsonlite "3.3.2"
keras "3.4.3"
kernlab "3.4.3"
KernSmooth "3.4.3"
knitr "3.4.3"
ks "3.4.3"
labeling "3.4.3"
lambda.r "3.4.3"
lattice "3.4.3"
latticeExtra "3.4.3"
lava "3.4.3"
lazyeval "3.4.3"
limma "3.4.3"
limSolve "3.4.3"
lme4 "3.4.3"
lmerTest "3.4.3"
lmtest "3.4.3"
logcondens "3.4.3"
lpSolve "3.4.3"
lubridate "3.4.3"
magrittr "3.4.3"
manhattanly "3.4.3"
maps "3.4.3"
markdown "3.4.3"
MASS "3.4.3"
Matrix "3.4.3"
matrixStats "3.4.3"
mclust "3.4.3"
memoise "3.4.3"
MetaIntegrator "3.4.3"
methods "3.4.3"
Metrics "3.4.3"
mgcv "3.4.3"
microbenchmark "3.4.3"
MicrosoftR "3.4.2"
mime "3.4.3"
minqa "3.4.3"
mlbench "3.4.3"
mnormt "3.4.3"
ModelMetrics "3.4.3"
modelr "3.4.3"
multicool "3.4.3"
multtest "3.4.3"
munsell "3.4.3"
mvtnorm "3.4.3"
nlme "3.4.3"
nloptr "3.4.3"
nnet "3.4.3"
numDeriv "3.4.3"
openssl "3.4.3"
org.Hs.eg.db "3.4.3"
parallel "3.4.3"
pbdZMQ "3.4.3"
pcaMethods "3.4.3"
pheatmap "3.4.3"
pillar "3.4.3"
pkgconfig "3.4.3"
plogr "3.4.3"
plotly "3.4.3"
plyr "3.4.3"
png "3.3.2"
polynom "3.4.3"
pracma "3.4.3"
praise "3.4.3"
preprocessCore "3.4.3"
prettydoc "3.4.3"
prettyunits "3.4.3"
pROC "3.4.3"
processx "3.4.3"
prodlim "3.4.3"
progress "3.4.3"
proxy "3.4.3"
psych "3.4.3"
purrr "3.4.3"
quadprog "3.4.3"
quantmod "3.4.3"
R.methodsS3 "3.4.3"
R.oo "3.4.3"
R.utils "3.4.3"
R6 "3.3.2"
randomForest "3.4.3"
ranger "3.4.3"
rbokeh "3.4.3"
RColorBrewer "3.4.3"
Rcpp "3.4.3"
RcppEigen "3.4.3"
RcppRoll "3.4.3"
RCurl "3.4.3"
readbitmap "3.4.3"
readr "3.4.3"
readxl "3.4.3"
recipes "3.4.3"
rematch "3.4.3"
repr "3.4.3"
reprex "3.4.3"
reshape "3.4.3"
reshape2 "3.4.3"
reticulate "3.4.3"
RevoIOQ "3.4.3"
RevoMods "3.3.3"
RevoUtils "3.4.3"
RevoUtilsMath "3.2.2"
rgexf "3.4.3"
rhdf5 "3.4.3"
rlang "3.4.3"
rmarkdown "3.4.3"
rmeta "3.4.3"
Rmisc "3.4.3"
RMySQL "3.4.3"
robustbase "3.4.3"
ROCR "3.4.3"
Rook "3.4.3"
rpart "3.4.3"
rprojroot "3.4.3"
RSQLite "3.4.3"
rstudioapi "3.4.3"
Rttf2pt1 "3.4.3"
RUnit "3.3.1"
rvcheck "3.4.3"
rvest "3.4.3"
S4Vectors "3.4.3"
scales "3.4.3"
selectr "3.4.3"
sfsmisc "3.4.3"
shiny "3.4.3"
snow "3.4.3"
snplist "3.4.3"
sourcetools "3.4.3"
sp "3.4.3"
spam "3.4.3"
spatial "3.4.3"
splines "3.4.3"
splus2R "3.4.3"
stats "3.4.3"
stats4 "3.4.3"
stringi "3.4.3"
stringr "3.4.3"
survival "3.4.3"
sva "3.4.3"
tcltk "3.4.3"
tensorflow "3.4.3"
testthat "3.4.3"
tfruns "3.4.3"
tibble "3.4.3"
tidyr "3.4.3"
tidyselect "3.4.3"
tidyverse "3.4.3"
timeDate "3.4.3"
tools "3.4.3"
TTR "3.4.3"
utf8 "3.4.3"
utils "3.4.3"
uuid "3.4.3"
vcd "3.4.3"
verification "3.4.3"
viridis "3.4.3"
viridisLite "3.4.3"
visNetwork "3.4.3"
whisker "3.4.3"
withr "3.4.3"
xgboost "3.4.3"
XML "3.4.3"
xml2 "3.4.3"
xtable "3.4.3"
xts "3.4.3"
yaml "3.4.3"
zeallot "3.4.3"
zlibbioc "3.4.3"
zoo "3.4.3"
Conda environment (for both python and R packages)¶
- Most of the python packages are not needed for this paper's analyses
- The conda environment (including both python and R packages) is described in the env_list.txt and environment.yml files
Figure 1 Mouse Analysis¶
This code reproduces the analysis of Figure 1, showing that "Blood genome-wide RNA expression accurately discriminates early vs. late M.tb infection time periods in C57BL/6 mice"
For review, this code requires reviewer access for the accession number of this paper. To reproduce this analysis with these data, please download the SOFT file for the full family data from the accession page (Scope: Family; Format: SOFT; Amount: Full; GEO accession: GSE124688; click "GO") using the Reviewer access token.
Place this SOFT file in the data/GSE124688 folder and rename it "GSE124688_family.soft"
Load Libraries¶
In [223]:
if (!require("preprocessCore")) {
source("https://bioconductor.org/biocLite.R")
biocLite("preprocessCore")
library("preprocessCore")
}
source("https://bioconductor.org/biocLite.R")
if (!require("Biobase")) {
biocLite("Biobase")
library("Biobase")
}
if (!require("GEOquery")) {
biocLite("GEOquery")
library("GEOquery")
}
if (!require("ggplot2")) {
install.packages("ggplot2")
library("ggplot2")
}
if (!require("randomForest")) {
install.packages("randomForest")
library("randomForest")
}
if (!require("cowplot")) {
install.packages("cowplot")
library("cowplot")
}
if (!require("pROC")) {
# pROC 1.12.0 is required, and may not be the default installation:
packageUrl<- "https://cran.r-project.org/src/contrib/Archive/pROC/pROC_1.12.0.tar.gz"
install.packages(packageUrl, repos=NULL, type='source')
library("pROC")
}
Bioconductor version 3.6 (BiocInstaller 1.28.0), ?biocLite for help A new version of Bioconductor is available after installing the most recent version of R; see http://bioconductor.org/install
Load and preprocess the data¶
In [ ]:
# In case this variable was not defined above, please do so here.
path="." # this is the TB repository folder
In [178]:
source("utils_submission.R")
mouse.path = paste(path, "/data/GSE124688", sep="")
soft.mouse.file.name = "GSE124688_family.soft"
mouse.soft = getGEO(filename=paste(mouse.path, soft.mouse.file.name, sep="/"))
gsmlist = GSMList(mouse.soft)
Reading file.... Parsing.... Found 27 entities... GPL6887 (1 of 28 entities) GSM3541213 (2 of 28 entities) GSM3541214 (3 of 28 entities) GSM3541215 (4 of 28 entities) GSM3541216 (5 of 28 entities) GSM3541217 (6 of 28 entities) GSM3541218 (7 of 28 entities) GSM3541219 (8 of 28 entities) GSM3541220 (9 of 28 entities) GSM3541221 (10 of 28 entities) GSM3541222 (11 of 28 entities) GSM3541223 (12 of 28 entities) GSM3541224 (13 of 28 entities) GSM3541225 (14 of 28 entities) GSM3541226 (15 of 28 entities) GSM3541227 (16 of 28 entities) GSM3541228 (17 of 28 entities) GSM3541229 (18 of 28 entities) GSM3541230 (19 of 28 entities) GSM3541231 (20 of 28 entities) GSM3541232 (21 of 28 entities) GSM3541233 (22 of 28 entities) GSM3541234 (23 of 28 entities) GSM3541235 (24 of 28 entities) GSM3541236 (25 of 28 entities) GSM3541237 (26 of 28 entities) GSM3541238 (27 of 28 entities)
In [ ]:
# Make the data matrix from the VALUE columns from each GSM, a matrix of detection Pvalues and the pheno data
# Parse the expression matrix
mouse.exprs <- do.call('cbind',lapply(gsmlist,function(x)
{tab <- Table(x)
return(tab$VALUE)
}))
mouse.exprs <- data.frame(apply(mouse.exprs,2,function(x) {as.numeric(as.character(x))}))
rownames(mouse.exprs) = Table(gsmlist[[1]])$ID_REF
# Parse the Detection P value matrix
detectPval.matrix = do.call('cbind',lapply(gsmlist,function(x)
{tab <- Table(x)
return(tab[, "Detection Pval"])
}))
detectPval.matrix <- data.frame(apply(detectPval.matrix,2,function(x) {as.numeric(as.character(x))}))
rownames(detectPval.matrix) = Table(gsmlist[[1]])$ID_REF
# Parse the pheno table
pheno = do.call('rbind', lapply(gsmlist, function(x)
{chars = Meta(x)$characteristics_ch1
newchars = chars
newnames = chars
i = 1
for (char in chars) {
new.name = strsplit(char, split=":")[[1]][1]
new.data = as.character(sapply(char, function(x) {return(trimws(strsplit(x, split=":")[[1]][2]))}))
newchars[i] = new.data
newnames[i] = new.name
i = i + 1
}
return(newchars)}
))
colnames(pheno) = c("strain", "infection.status", "time.point.days", "tissue", "gender")
pheno.data = data.frame(pheno)
pheno.data$time.point.days = as.numeric(as.character(pheno.data$time.point.days))
In [220]:
# Filter probes with a detection P value <= 0.01 in at least 10% of mice
det_pval_thresh = 0.01
PAL.10 = apply(detectPval.matrix <= det_pval_thresh, 1, mean) >= 0.10
mouse.exprs.fil = mouse.exprs[PAL.10,]
# Quantile normalize the data
mouse.exprs.qn = as.data.frame(normalize.quantiles(as.matrix(mouse.exprs.fil)))
colnames(mouse.exprs.qn) = colnames(mouse.exprs.fil)
rownames(mouse.exprs.qn) = rownames(mouse.exprs.fil)
# Set all values <10 to 10
mouse.exprs.qn[mouse.exprs.qn < 10] = 10
# Filter probes by two-fold change in expression from the median in at least 10% of samples (10% 2 fold above, or 10% 2 fold below)
med.exprs = mouse.exprs.qn / apply(mouse.exprs.qn, 1, median)
var.genes = (apply((med.exprs > 2.00) , 1, mean) > 0.10) | (apply((med.exprs < 0.50) , 1, mean) > 0.10)
mouse.exprs.qn = mouse.exprs.qn[var.genes,]
# Log2 transform data
mouse.exprs.log = log2(mouse.exprs.qn + 1)
In [224]:
dim(mouse.exprs.log)
- 1650
- 26
Figure 1A (Principle Component Analysis of all mice)¶
In [225]:
PCA.mouse.all = prcomp(t(mouse.exprs.log), scale.=T)
gg.PCA.mouse.all = as.data.frame(PCA.mouse.all$x[,1:5])
gg.PCA.mouse.all$infection.status = pheno.data$infection.status
In [264]:
# Principle Component Analysis of Infected vs. Uninfected
std_dev = PCA.mouse.all$sdev
pr_var = std_dev^2
prop_varex = pr_var / sum(pr_var)
Infect.PCA.plot = ggplot(data=gg.PCA.mouse.all, aes(x=PC1, y= PC2)) +
geom_point(aes(col=infection.status), size=1) + scale_color_manual(name="",
labels=c("M.tb infected", "Naive"),
values=c("Mtb"="blue", "Naive"="darkorange")) +
theme(legend.position=c(0.55,0.9)) +
labs(x=paste("PC 1 [", format(prop_varex[1]*100, digits=3, nsmall=1), "% of Variance]", sep=""),
y=paste("PC 2 [", format(prop_varex[2]*100, digits=3, nsmall=1), "% of Variance]", sep="")) +
theme(legend.position=c(0.5,0.90))
Infect.PCA.plot
Figure 1B (Principle Component Analysis of infected mice)¶
In [268]:
mouse.exprs.Mtb = mouse.exprs.log[,pheno.data$infection.status == "Mtb"]
PCA.mouse.Mtb = prcomp(t(mouse.exprs.Mtb), scale.=T)
gg.PCA.mouse.Mtb = as.data.frame(PCA.mouse.Mtb$x[,1:5])
std_dev = PCA.mouse.Mtb$sdev
pr_var = std_dev^2
prop_varex = pr_var / sum(pr_var)
stage = ifelse(pheno.data[pheno.data$infection.status=="Mtb",]$time.point.days <= 60,
"early",
"late")
gg.PCA.mouse.Mtb$stage = as.factor(stage)
Time.PCA.plot = ggplot(data=gg.PCA.mouse.Mtb, aes(x=PC1, y= PC2)) +
geom_point(aes(col=stage), size=1) + scale_color_manual(name="",
labels=c("Early (30 - 60 days)", "Late (90 - 150 days)"),
values=c("early"="black", "late"="red")) +
theme(legend.position=c(0.50,0.90), legend.text = element_text(size=12)) +
labs(x=paste("PC 1 [", format(prop_varex[1]*100, digits=3, nsmall=1), "% of Variance]", sep=""),
y=paste("PC 2 [", format(prop_varex[2]*100, digits=3, nsmall=1), "% of Variance]", sep="")) +
theme(legend.position=c(0.4,0.90))
Time.PCA.plot
Figure 1C (Classification of early vs. late time periods post-infection)¶
In [250]:
set.seed(100)
mouse.table.class = as.data.frame(t(mouse.exprs.log[,pheno.data$infection.status == "Mtb"]))
mouse.table.class$stage = as.factor(stage)
mouse.rf.class = randomForest(stage~., data=mouse.table.class)
In [253]:
class.plot = my.roc(1 - mouse.rf.class$votes[,2], mouse.table.class$stage, "early")
gg.class.plot = ggroc(list(mouse=class.plot), legacy.axes=TRUE) +
geom_abline(intercept = 0, slope = 1, color = "lightgrey", size = 0.25) +
ggtitle("Early vs. Late Time Period") + theme(plot.title = element_text(size=12, face="plain")) +
scale_color_manual(name="M.tb Infected Mice",
labels=expression("AUC 0.99, p ="~1.6~ "x" ~10^{-5} ~ " "),
values=c("mouse"="black")) +
theme(legend.position=c(0.30,0.20), legend.title = element_text(size=12), legend.text = element_text(size=11))
gg.class.plot
[1] "This is the AUC:" Area under the curve: 0.9896 [1] "This is the AUC p-value:" [1] 1.58768e-05 [1] "This is the AUC 95% Confidence Interval" 95% CI: 0.9607-1 (DeLong)
Figure 1D (Regression of time post-infection)¶
In [256]:
mouse.table.regress = as.data.frame(t(mouse.exprs.log[,pheno.data$infection.status == "Mtb"]))
mouse.table.regress$time.point.days = pheno.data[pheno.data$infection.status=="Mtb",]$time.point.days
set.seed(100)
C57.rf.regres = randomForest(time.point.days~., data=mouse.table.regress, importance=T)
predictions = data.frame(C57.predicted=C57.rf.regres$predicted, C57.time.point.days = mouse.table.regress$time.point.days)
set.seed(100)
regress.plot = ggplot(predictions, aes(C57.time.point.days, C57.predicted))+
geom_point() +
geom_smooth() + theme_bw() +
labs(x="Days Post Infection", y="Predicted Days Post Infection", size=14) +
ggtitle("Regression on Time Post Infection") +
theme(plot.title = element_text(hjust = 0.5, size=12)) +
scale_x_continuous(breaks=c(30, 60, 90, 120, 150)) +
scale_y_continuous(breaks=c(30, 60, 90, 120, 150))
regress.plot
Warning message in randomForest.default(m, y, ...): “The response has five or fewer unique values. Are you sure you want to do regression?”`geom_smooth()` using method = 'loess'
Figure 2 Macaque Analysis¶
- This code reproduces the analysis of Figure 2, showing that "Blood genome-wide RNA expression discriminates early vs. late M.tb infection time periods in cynomolgus macaques"
Install and Load Required Libraries¶
In [87]:
source("https://bioconductor.org/biocLite.R")
if (!require("Biobase")) {
biocLite("Biobase")
library("Biobase")
}
if (!require("GEOquery")) {
biocLite("GEOquery")
library("GEOquery")
}
if (!require("sva")) {
biocLite("sva")
library("sva")
}
if (!require("ranger")) {
install.packages("ranger")
library("ranger")
}
if (!require("ggplot2")) {
install.packages("ggplot2")
library("ggplot2")
}
if (!require("glmnet")) {
install.packages("glmnet")
library("glmnet")
}
if (!require("gbm")) {
install.packages("gbm")
library("gbm")
}
if (!require("kernlab")) {
install.packages("kernlab")
library("kernlab")
}
if (!require("caret")) {
install.packages("caret")
library("caret")
}
if (!require("dplyr")) {
install.packages("dplyr")
library("dplyr")
}
if (!require("ggsignif")) {
install.packages("ggsignif")
library("ggsignif")
}
if (!require("doParallel")) {
install.packages("doParallel")
library("doParallel")
}
if (!require("cowplot")) {
install.packages("cowplot")
library("cowplot")
}
if (!require("pROC")) {
# pROC 1.12.0 is required, and may not be the default installation:
packageUrl<- "https://cran.r-project.org/src/contrib/Archive/pROC/pROC_1.12.0.tar.gz"
install.packages(packageUrl, repos=NULL, type='source')
library("pROC")
}
Bioconductor version 3.6 (BiocInstaller 1.28.0), ?biocLite for help
A new version of Bioconductor is available after installing the most recent
version of R; see http://bioconductor.org/install
Loading required package: gbm
Loading required package: survival
Attaching package: ‘survival’
The following object is masked from ‘package:boot’:
aml
The following object is masked from ‘package:caret’:
cluster
Loading required package: splines
Loaded gbm 2.1.3
Loading required package: kernlab
Attaching package: ‘kernlab’
The following object is masked from ‘package:CircStats’:
rvm
The following object is masked from ‘package:ggplot2’:
alpha
Loading required package: ggsignif
Load the data¶
In [80]:
source("utils_submission.R")
In [16]:
monkey.path = paste(path, "/data/GSE84152", sep="")
In [ ]:
monkey.gset = getGEO(filename=paste(monkey.path, "GSE84152_series_matrix.txt.gz", sep="/"),
destdir=monkey.path)
In [22]:
monkey.gset
ExpressionSet (storageMode: lockedEnvironment)
assayData: 47323 features, 470 samples
element names: exprs
protocolData: none
phenoData
sampleNames: GSM2227793 GSM2227794 ... GSM2228262 (470 total)
varLabels: title geo_accession ... wbc:ch1 (95 total)
varMetadata: labelDescription
featureData
featureNames: ILMN_1343291 ILMN_1343295 ... ILMN_3311190 (47323
total)
fvarLabels: ID Species ... GB_ACC (30 total)
fvarMetadata: Column Description labelDescription
experimentData: use 'experimentData(object)'
Annotation: GPL10558
In [23]:
raw.expres = read.table(file = paste(monkey.path, "GSE84152_non-normalized.txt", sep="/"), header=T , sep="\t")
Process the phenotype table, filter probes expressed in at least 5% of samples (Detection Pvalue < 0.01)¶
In [26]:
pheno = filter.monkey.pheno(pData(monkey.gset))
title ChIP hyb.chamber dataset synchroset monkeyid time.point
GSM2227793 M16_150 1 1 Training No M16 150
GSM2227794 M17_3 1 1 Training No M17 3
GSM2227795 M8_Pre2 1 1 Training No M8 1
GSM2227796 M19_56 1 1 Training No M19 56
GSM2227797 M19_90 1 1 Training No M19 90
GSM2227798 M1_7 1 1 Training No M1 7
GSM2227799 M18_20 1 1 Training No M18 20
GSM2227800 M15_90 1 1 Training No M15 90
GSM2227801 M1_42 1 1 Training No M1 42
GSM2227802 M13_180 1 1 Training Yes M13 180
GSM2227803 M17_10 1 1 Training No M17 10
GSM2227804 M18_10 1 1 Training No M18 10
GSM2227805 M6_120 2 1 Training No M6 120
GSM2227806 M14_90 2 1 Training No M14 90
GSM2227807 M15_20 2 1 Training No M15 20
GSM2227808 M17_120 2 1 Training No M17 120
GSM2227809 M2_42 2 1 Training No M2 42
GSM2227810 M9_120 2 1 Training No M9 120
GSM2227811 M17_7 2 1 Training No M17 7
GSM2227812 M9_30 2 1 Training No M9 30
GSM2227813 M16_120 2 1 Training No M16 120
GSM2227814 M3_180 2 1 Training Yes M3 180
GSM2227815 M3_30 2 1 Training No M3 30
GSM2227816 M19_150 2 1 Training Yes M19 150
GSM2227817 M19_3 3 1 Training No M19 3
GSM2227818 M1_20 3 1 Training No M1 20
GSM2227819 M11_7 3 1 Training No M11 7
GSM2227820 M18_120 3 1 Training No M18 120
GSM2227821 M14_3 3 1 Training No M14 3
GSM2227822 M7_3 3 1 Training No M7 3
GSM2227823 M8_150 3 1 Training No M8 150
GSM2227824 M4_Pre2 3 1 Training No M4 1
GSM2227825 M4_56 3 1 Training No M4 56
GSM2227826 M17_Pre1 3 1 Training Yes M17 0
GSM2227827 M9_90 3 1 Training No M9 90
GSM2227828 M1_56 4 1 Training No M1 56
GSM2227829 M18_Pre1 4 1 Training Yes M18 0
GSM2227830 M13_Pre2 4 1 Training No M13 1
GSM2227831 M3_Pre2 4 1 Training No M3 1
GSM2227832 M4_20 4 1 Training No M4 20
GSM2227833 M16_180 4 1 Training Yes M16 180
GSM2227834 M18_30 4 1 Training No M18 30
GSM2227835 M6_42 4 1 Training No M6 42
GSM2227836 M2_20 4 1 Training No M2 20
GSM2227837 M14_30 4 1 Training No M14 30
GSM2227838 M14_Pre2 4 1 Training No M14 1
GSM2227839 M5_150 4 1 Training No M5 150
GSM2227840 M8_Pre1 5 2 Training Yes M8 0
GSM2227841 M13_42 5 2 Training No M13 42
GSM2227842 M19_30 5 2 Training No M19 30
GSM2227843 M12_150 5 2 Training No M12 150
GSM2227844 M7_56 5 2 Training No M7 56
GSM2227845 M15_3 5 2 Training No M15 3
GSM2227846 M15_7 5 2 Training No M15 7
GSM2227847 M1_180 5 2 Training Yes M1 180
GSM2227848 M5_42 5 2 Training No M5 42
GSM2227849 M3_Pre1 5 2 Training Yes M3 0
GSM2227850 M13_7 5 2 Training No M13 7
GSM2227851 M4_3 5 2 Training No M4 3
GSM2227852 M14_10 6 2 Training No M14 10
GSM2227853 M10_Pre1 6 2 Training Yes M10 0
GSM2227854 M5_10 6 2 Training No M5 10
GSM2227855 M2_150 6 2 Training No M2 150
GSM2227856 M3_150 6 2 Training No M3 150
GSM2227857 M12_120 6 2 Training No M12 120
GSM2227858 M7_42 6 2 Training No M7 42
GSM2227859 M16_30 6 2 Training No M16 30
GSM2227860 M2_3 6 2 Training No M2 3
GSM2227861 M19_120 6 2 Training No M19 120
GSM2227862 M5_7 6 2 Training No M5 7
GSM2227863 M4_10 6 2 Training No M4 10
GSM2227864 M13_90 7 2 Training No M13 90
GSM2227865 M3_120 7 2 Training No M3 120
GSM2227866 M18_90 7 2 Training No M18 90
GSM2227867 M12_7 7 2 Training No M12 7
GSM2227868 M17_150 7 2 Training No M17 150
GSM2227869 M4_150 7 2 Training No M4 150
GSM2227870 M6_Pre1 7 2 Training Yes M6 0
GSM2227871 M13_150 7 2 Training No M13 150
GSM2227872 M15_120 7 2 Training No M15 120
GSM2227873 M18_56 7 2 Training No M18 56
GSM2227874 M4_Pre1 7 2 Training Yes M4 0
GSM2227875 M9_42 7 2 Training No M9 42
GSM2227876 M9_10 8 2 Training No M9 10
GSM2227877 M6_150 8 2 Training No M6 150
GSM2227878 M8_120 8 2 Training No M8 120
GSM2227879 M14_20 8 2 Training No M14 20
GSM2227880 M9_20 8 2 Training No M9 20
GSM2227881 M3_7 8 2 Training No M3 7
GSM2227882 M18_Pre2 8 2 Training No M18 1
GSM2227883 M3_20 8 2 Training No M3 20
GSM2227884 M10_90 8 2 Training Yes M10 90
GSM2227885 M1_Pre1 8 2 Training Yes M1 0
GSM2227886 M16_90 8 2 Training No M16 90
GSM2227887 M12_Pre1 9 3 Training Yes M12 0
GSM2227888 M18_7 9 3 Training No M18 7
GSM2227889 M5_90 9 3 Training No M5 90
GSM2227890 M11_56 9 3 Training No M11 56
GSM2227891 M10_7 9 3 Training No M10 7
GSM2227892 M8_3 9 3 Training No M8 3
GSM2227893 M7_180 9 3 Training Yes M7 180
GSM2227894 M6_30 9 3 Training No M6 30
GSM2227895 M13_56 9 3 Training No M13 56
GSM2227896 M7_Pre1 9 3 Training Yes M7 0
GSM2227897 M1_Pre2 9 3 Training No M1 1
GSM2227898 M2_56 9 3 Training No M2 56
GSM2227899 M8_30 10 3 Training No M8 30
GSM2227900 M15_150 10 3 Training No M15 150
GSM2227901 M5_3 10 3 Training No M5 3
GSM2227902 M7_10 10 3 Training No M7 10
GSM2227903 M7_30 10 3 Training No M7 30
GSM2227904 M7_7 10 3 Training No M7 7
GSM2227905 M6_90 10 3 Training No M6 90
GSM2227906 M13_10 10 3 Training No M13 10
GSM2227907 M16_Pre1 10 3 Training Yes M16 0
GSM2227908 M11_120 10 3 Training No M11 120
GSM2227909 M14_150 10 3 Training No M14 150
GSM2227910 M13_3 10 3 Training No M13 3
GSM2227911 M16_Pre2 11 3 Training No M16 1
GSM2227912 M12_180 11 3 Training Yes M12 180
GSM2227913 M13_120 11 3 Training No M13 120
GSM2227914 M1_90 11 3 Training No M1 90
GSM2227915 M12_42 11 3 Training No M12 42
GSM2227916 M3_3 11 3 Training No M3 3
GSM2227917 M15_42 11 3 Training No M15 42
GSM2227918 M5_180 11 3 Training Yes M5 180
GSM2227919 M7_90 11 3 Training No M7 90
GSM2227920 M15_10 11 3 Training No M15 10
GSM2227921 M2_Pre2 11 3 Training No M2 1
GSM2227922 M7_Pre2 12 3 Training No M7 1
GSM2227923 M5_56 12 3 Training No M5 56
GSM2227924 M6_20 12 3 Training No M6 20
GSM2227925 M10_30 12 3 Training No M10 30
GSM2227926 M9_180 12 3 Training Yes M9 180
GSM2227927 M6_3 12 3 Training No M6 3
GSM2227928 M10_56 12 3 Training No M10 56
GSM2227929 M16_10 12 3 Training No M16 10
GSM2227930 M10_10 12 3 Training No M10 10
GSM2227931 M10_3 12 3 Training No M10 3
GSM2227932 M15_Pre2 12 3 Training No M15 1
GSM2227933 M9_56 12 3 Training No M9 56
GSM2227934 M15_30 13 4 Training No M15 30
GSM2227935 M3_90 13 4 Training No M3 90
GSM2227936 M4_7 13 4 Training No M4 7
GSM2227937 M12_3 13 4 Training No M12 3
GSM2227938 M19_7 13 4 Training No M19 7
GSM2227939 M7_20 13 4 Training No M7 20
GSM2227940 M14_7 13 4 Training No M14 7
GSM2227941 M13_Pre1 13 4 Training Yes M13 0
GSM2227942 M11_10 13 4 Training No M11 10
GSM2227943 M13_30 13 4 Training No M13 30
GSM2227944 M17_56 13 4 Training No M17 56
GSM2227945 M16_56 13 4 Training No M16 56
GSM2227946 M9_7 14 4 Training No M9 7
GSM2227947 M1_150 14 4 Training No M1 150
GSM2227948 M1_3 14 4 Training No M1 3
GSM2227949 M5_20 14 4 Training No M5 20
GSM2227950 M12_20 14 4 Training No M12 20
GSM2227951 M14_42 14 4 Training No M14 42
GSM2227952 M8_20 14 4 Training No M8 20
GSM2227953 M8_10 14 4 Training No M8 10
GSM2227954 M19_42 14 4 Training No M19 42
GSM2227955 M4_42 14 4 Training No M4 42
GSM2227956 M19_20 14 4 Training No M19 20
GSM2227957 M15_56 14 4 Training No M15 56
GSM2227958 M18_42 15 4 Training No M18 42
GSM2227959 M6_7 15 4 Training No M6 7
GSM2227960 M2_180 15 4 Training Yes M2 180
GSM2227961 M19_10 15 4 Training No M19 10
GSM2227962 M4_30 15 4 Training No M4 30
GSM2227963 M16_42 15 4 Training No M16 42
GSM2227964 M3_42 15 4 Training No M3 42
GSM2227965 M9_150 15 4 Training No M9 150
GSM2227966 M11_Pre1 15 4 Training Yes M11 0
GSM2227967 M11_3 15 4 Training No M11 3
GSM2227968 M8_56 15 4 Training No M8 56
GSM2227969 M17_180 15 4 Training Yes M17 180
GSM2227970 M18_150 16 4 Training Yes M18 150
GSM2227971 M4_90 16 4 Training No M4 90
GSM2227972 M15_Pre1 16 4 Training Yes M15 0
GSM2227973 M15_180 16 4 Training Yes M15 180
GSM2227974 M1_30 16 4 Training No M1 30
GSM2227975 M11_150 16 4 Training No M11 150
GSM2227976 M4_120 16 4 Training No M4 120
GSM2227977 M16_7 16 4 Training No M16 7
GSM2227978 M8_42 16 4 Training No M8 42
GSM2227979 M12_10 16 4 Training No M12 10
GSM2227980 M2_90 16 4 Training No M2 90
GSM2227981 M2_7 16 4 Training No M2 7
GSM2227982 M1_10 17 5 Training No M1 10
GSM2227983 M11_42 17 5 Training No M11 42
GSM2227984 M7_150 17 5 Training No M7 150
GSM2227985 M14_56 17 5 Training No M14 56
GSM2227986 M10_20 17 5 Training No M10 20
GSM2227987 M11_180 17 5 Training Yes M11 180
GSM2227988 M5_30 17 5 Training No M5 30
GSM2227989 M12_Pre2 17 5 Training No M12 1
GSM2227990 M2_Pre1 17 5 Training Yes M2 0
GSM2227991 M9_Pre1 17 5 Training Yes M9 0
GSM2227992 M6_Pre2 17 5 Training No M6 1
GSM2227993 M1_120 17 5 Training No M1 120
GSM2227994 M5_Pre2 18 5 Training No M5 1
GSM2227995 M2_10 18 5 Training No M2 10
GSM2227996 M9_3 18 5 Training No M9 3
GSM2227997 M7_120 18 5 Training No M7 120
GSM2227998 M5_Pre1 18 5 Training Yes M5 0
GSM2227999 M2_30 18 5 Training No M2 30
GSM2228000 M16_3 18 5 Training No M16 3
GSM2228001 M11_30 18 5 Training No M11 30
GSM2228002 M10_42 18 5 Training No M10 42
GSM2228003 M12_30 18 5 Training No M12 30
GSM2228004 M12_56 18 5 Training No M12 56
GSM2228005 M17_90 18 5 Training No M17 90
GSM2228006 M14_120 19 5 Training No M14 120
GSM2228007 M8_7 19 5 Training No M8 7
GSM2228008 M14_Pre1 19 5 Training Yes M14 0
GSM2228009 M12_90 19 5 Training No M12 90
GSM2228010 M8_90 19 5 Training No M8 90
GSM2228011 M8_180 19 5 Training Yes M8 180
GSM2228012 M4_180 19 5 Training Yes M4 180
GSM2228013 M3_10 19 5 Training No M3 10
GSM2228014 M10_Pre2 19 5 Training No M10 1
GSM2228015 M6_180 19 5 Training Yes M6 180
GSM2228016 M3_56 19 5 Training No M3 56
GSM2228017 M5_120 20 5 Training No M5 120
GSM2228018 M17_30 20 5 Training No M17 30
GSM2228019 M16_20 20 5 Training No M16 20
GSM2228020 M11_20 20 5 Training No M11 20
GSM2228021 M17_42 20 5 Training No M17 42
GSM2228022 M14_180 20 5 Training Yes M14 180
GSM2228023 M13_20 20 5 Training No M13 20
GSM2228024 M11_90 20 5 Training No M11 90
GSM2228025 M17_20 20 5 Training No M17 20
GSM2228026 M6_56 20 5 Training No M6 56
GSM2228027 M2_120 20 5 Training No M2 120
GSM2228028 M19_Pre1 20 5 Training Yes M19 0
GSM2228029 M6_10 21 6 Training No M6 10
GSM2228030 M18_3 21 6 Training No M18 3
GSM2228031 M24_Pre1 22 7 Test Yes M24 0
GSM2228032 M32_120 22 7 Test No M32 120
GSM2228033 M36_10 22 7 Test No M36 10
GSM2228034 M20_56 22 7 Test No M20 56
GSM2228035 M23_90 22 7 Test Yes M23 90
GSM2228036 M24_90 22 7 Test No M24 90
GSM2228037 M34_42 22 7 Test No M34 42
GSM2228038 M29_90 22 7 Test No M29 90
GSM2228039 M28_10 23 7 Test No M28 10
GSM2228040 M33_7 23 7 Test No M33 7
GSM2228041 M28_180 23 7 Test Yes M28 180
GSM2228042 M31_10 23 7 Test No M31 10
GSM2228043 M33_3 23 7 Test No M33 3
GSM2228044 M29_20 23 7 Test No M29 20
GSM2228045 M23_Pre1 23 7 Test Yes M23 0
GSM2228046 M20_180 23 7 Test Yes M20 180
GSM2228047 M28_150 23 7 Test No M28 150
GSM2228048 M25_Pre2 23 7 Test No M25 1
GSM2228049 M33_150 24 7 Test No M33 150
GSM2228050 M20_150 24 7 Test No M20 150
GSM2228051 M22_150 24 7 Test No M22 150
GSM2228052 M23_7 24 7 Test No M23 7
GSM2228053 M36_42 24 7 Test No M36 42
GSM2228054 M23_42 24 7 Test No M23 42
GSM2228055 M29_150 24 7 Test No M29 150
GSM2228056 M34_30 24 7 Test No M34 30
GSM2228057 M26_56 24 7 Test No M26 56
GSM2228058 M30_56 24 7 Test No M30 56
GSM2228059 M27_90 24 7 Test No M27 90
GSM2228060 M27_10 24 7 Test No M27 10
GSM2228061 M22_10 25 7 Test No M22 10
GSM2228062 M29_42 25 7 Test No M29 42
GSM2228063 M38_3 25 7 Test No M38 3
GSM2228064 M28_Pre1 25 7 Test Yes M28 0
GSM2228065 M38_90 25 7 Test No M38 90
GSM2228066 M21_180 25 7 Test Yes M21 180
GSM2228067 M29_Pre1 25 7 Test Yes M29 0
GSM2228068 M34_3 25 7 Test No M34 3
GSM2228069 M25_90 25 7 Test No M25 90
GSM2228070 M22_30 25 7 Test No M22 30
GSM2228071 M25_Pre1 25 7 Test Yes M25 0
GSM2228072 M20_10 26 8 Test No M20 10
GSM2228073 M29_180 26 8 Test Yes M29 180
GSM2228074 M31_56 26 8 Test No M31 56
GSM2228075 M27_20 26 8 Test No M27 20
GSM2228076 M23_10 26 8 Test No M23 10
GSM2228077 M30_180 26 8 Test Yes M30 180
GSM2228078 M26_42 26 8 Test No M26 42
GSM2228079 M23_Pre2 26 8 Test No M23 1
GSM2228080 M35_56 26 8 Test No M35 56
GSM2228081 M26_Pre2 26 8 Test No M26 1
GSM2228082 M36_20 26 8 Test No M36 20
GSM2228083 M30_3 27 8 Test No M30 3
GSM2228084 M28_90 27 8 Test No M28 90
GSM2228085 M30_42 27 8 Test No M30 42
GSM2228086 M34_10 27 8 Test No M34 10
GSM2228087 M37_Pre2 27 8 Test No M37 1
GSM2228088 M37_180 27 8 Test Yes M37 180
GSM2228089 M33_56 27 8 Test No M33 56
GSM2228090 M20_90 27 8 Test No M20 90
GSM2228091 M36_180 27 8 Test Yes M36 180
GSM2228092 M25_30 27 8 Test No M25 30
GSM2228093 M23_56 28 8 Test No M23 56
GSM2228094 M21_42 28 8 Test No M21 42
GSM2228095 M26_90 28 8 Test No M26 90
GSM2228096 M21_30 28 8 Test No M21 30
GSM2228097 M34_20 28 8 Test No M34 20
GSM2228098 M22_180 28 8 Test Yes M22 180
GSM2228099 M21_56 28 8 Test No M21 56
GSM2228100 M32_30 28 8 Test No M32 30
GSM2228101 M35_Pre1 28 8 Test Yes M35 0
GSM2228102 M26_180 29 8 Test Yes M26 180
GSM2228103 M32_Pre2 29 8 Test No M32 1
GSM2228104 M37_90 29 8 Test No M37 90
GSM2228105 M27_180 29 8 Test Yes M27 180
GSM2228106 M38_120 29 8 Test No M38 120
GSM2228107 M31_Pre2 29 8 Test No M31 1
GSM2228108 M30_20 29 8 Test No M30 20
GSM2228109 M37_Pre1 29 8 Test Yes M37 0
GSM2228110 M27_7 29 8 Test No M27 7
GSM2228111 M21_10 29 8 Test No M21 10
GSM2228112 M34_120 29 8 Test No M34 120
GSM2228113 M32_10 29 8 Test No M32 10
GSM2228114 M29_10 30 9 Test No M29 10
GSM2228115 M27_56 30 9 Test No M27 56
GSM2228116 M25_180 30 9 Test Yes M25 180
GSM2228117 M35_7 30 9 Test No M35 7
GSM2228118 M23_20 30 9 Test No M23 20
GSM2228119 M31_120 30 9 Test No M31 120
GSM2228120 M21_120 30 9 Test No M21 120
GSM2228121 M22_90 30 9 Test No M22 90
GSM2228122 M33_42 30 9 Test No M33 42
GSM2228123 M38_20 30 9 Test No M38 20
GSM2228124 M28_7 31 9 Test No M28 7
GSM2228125 M35_3 31 9 Test No M35 3
GSM2228126 M35_120 31 9 Test No M35 120
GSM2228127 M22_Pre1 31 9 Test Yes M22 0
GSM2228128 M20_120 31 9 Test No M20 120
GSM2228129 M28_42 31 9 Test No M28 42
GSM2228130 M28_20 31 9 Test No M28 20
GSM2228131 M35_90 31 9 Test No M35 90
GSM2228132 M31_150 31 9 Test No M31 150
GSM2228133 M29_56 31 9 Test No M29 56
GSM2228134 M29_30 31 9 Test No M29 30
GSM2228135 M35_150 32 9 Test No M35 150
GSM2228136 M30_150 32 9 Test No M30 150
GSM2228137 M27_Pre1 32 9 Test Yes M27 0
GSM2228138 M37_42 32 9 Test No M37 42
GSM2228139 M24_120 32 9 Test No M24 120
GSM2228140 M21_90 32 9 Test No M21 90
GSM2228141 M36_56 32 9 Test No M36 56
GSM2228142 M32_7 32 9 Test No M32 7
GSM2228143 M28_56 32 9 Test No M28 56
GSM2228144 M24_56 32 9 Test No M24 56
GSM2228145 M34_150 33 9 Test No M34 150
GSM2228146 M25_56 33 9 Test No M25 56
GSM2228147 M24_30 33 9 Test No M24 30
GSM2228148 M36_7 33 9 Test No M36 7
GSM2228149 M32_56 33 9 Test No M32 56
GSM2228150 M30_Pre2 33 9 Test No M30 1
GSM2228151 M25_120 33 9 Test No M25 120
GSM2228152 M32_180 33 9 Test Yes M32 180
GSM2228153 M33_120 33 9 Test No M33 120
GSM2228154 M23_30 33 9 Test No M23 30
GSM2228155 M26_7 34 10 Test No M26 7
GSM2228156 M35_Pre2 34 10 Test No M35 1
GSM2228157 M32_90 34 10 Test No M32 90
GSM2228158 M36_120 34 10 Test No M36 120
GSM2228159 M36_Pre2 34 10 Test No M36 1
GSM2228160 M38_56 34 10 Test No M38 56
GSM2228161 M30_10 34 10 Test No M30 10
GSM2228162 M29_120 34 10 Test No M29 120
GSM2228163 M24_7 34 10 Test No M24 7
GSM2228164 M25_20 34 10 Test No M25 20
GSM2228165 M38_42 35 10 Test No M38 42
GSM2228166 M37_30 35 10 Test No M37 30
GSM2228167 M30_120 35 10 Test No M30 120
GSM2228168 M24_10 35 10 Test No M24 10
GSM2228169 M22_Pre2 35 10 Test No M22 1
GSM2228170 M34_56 35 10 Test No M34 56
GSM2228171 M35_42 35 10 Test No M35 42
GSM2228172 M38_150 35 10 Test Yes M38 150
GSM2228173 M31_7 35 10 Test No M31 7
GSM2228174 M37_3 35 10 Test No M37 3
GSM2228175 M24_Pre2 35 10 Test No M24 1
GSM2228176 M30_30 35 10 Test No M30 30
GSM2228177 M26_150 36 10 Test No M26 150
GSM2228178 M32_3 36 10 Test No M32 3
GSM2228179 M37_20 36 10 Test No M37 20
GSM2228180 M33_180 36 10 Test Yes M33 180
GSM2228181 M35_20 36 10 Test No M35 20
GSM2228182 M28_120 36 10 Test No M28 120
GSM2228183 M36_90 36 10 Test No M36 90
GSM2228184 M29_Pre2 36 10 Test No M29 1
GSM2228185 M31_20 36 10 Test No M31 20
GSM2228186 M37_10 36 10 Test No M37 10
GSM2228187 M36_Pre1 37 10 Test Yes M36 0
GSM2228188 M32_20 37 10 Test No M32 20
GSM2228189 M32_42 37 10 Test No M32 42
GSM2228190 M31_180 37 10 Test Yes M31 180
GSM2228191 M34_Pre2 37 10 Test No M34 1
GSM2228192 M36_150 37 10 Test No M36 150
GSM2228193 M31_90 37 10 Test No M31 90
GSM2228194 M31_3 37 10 Test No M31 3
GSM2228195 M32_Pre1 37 10 Test Yes M32 0
GSM2228196 M24_180 37 10 Test Yes M24 180
GSM2228197 M31_42 37 10 Test No M31 42
GSM2228198 M38_Pre1 37 10 Test Yes M38 0
GSM2228199 M24_42 38 11 Test No M24 42
GSM2228200 M34_Pre1 38 11 Test Yes M34 0
GSM2228201 M37_56 38 11 Test No M37 56
GSM2228202 M27_120 38 11 Test No M27 120
GSM2228203 M26_120 38 11 Test No M26 120
GSM2228204 M25_150 38 11 Test No M25 150
GSM2228205 M33_10 38 11 Test No M33 10
GSM2228206 M31_Pre1 38 11 Test Yes M31 0
GSM2228207 M38_10 38 11 Test No M38 10
GSM2228208 M37_150 38 11 Test No M37 150
GSM2228209 M27_Pre2 38 11 Test No M27 1
GSM2228210 M34_7 39 11 Test No M34 7
GSM2228211 M20_42 39 11 Test No M20 42
GSM2228212 M37_120 39 11 Test No M37 120
GSM2228213 M21_Pre1 39 11 Test Yes M21 0
GSM2228214 M30_7 39 11 Test No M30 7
GSM2228215 M22_20 39 11 Test No M22 20
GSM2228216 M25_10 39 11 Test No M25 10
GSM2228217 M21_Pre2 39 11 Test No M21 1
GSM2228218 M22_42 39 11 Test No M22 42
GSM2228219 M38_7 39 11 Test No M38 7
GSM2228220 M33_Pre2 39 11 Test No M33 1
GSM2228221 M35_10 39 11 Test No M35 10
GSM2228222 M25_7 40 11 Test No M25 7
GSM2228223 M34_90 40 11 Test No M34 90
GSM2228224 M33_30 40 11 Test No M33 30
GSM2228225 M26_20 40 11 Test No M26 20
GSM2228226 M33_Pre1 40 11 Test Yes M33 0
GSM2228227 M38_Pre2 40 11 Test No M38 1
GSM2228228 M20_20 40 11 Test No M20 20
GSM2228229 M33_90 40 11 Test No M33 90
GSM2228230 M24_150 40 11 Test No M24 150
GSM2228231 M27_150 40 11 Test No M27 150
GSM2228232 M38_30 40 11 Test No M38 30
GSM2228233 M30_Pre1 40 11 Test Yes M30 0
GSM2228234 M36_3 41 11 Test No M36 3
GSM2228235 M20_7 41 11 Test No M20 7
GSM2228236 M37_7 41 11 Test No M37 7
GSM2228237 M35_180 41 11 Test Yes M35 180
GSM2228238 M26_30 41 11 Test No M26 30
GSM2228239 M22_120 41 11 Test No M22 120
GSM2228240 M32_150 41 11 Test No M32 150
GSM2228241 M22_7 41 11 Test No M22 7
GSM2228242 M24_20 41 11 Test No M24 20
GSM2228243 M33_20 41 11 Test No M33 20
GSM2228244 M28_30 42 12 Test No M28 30
GSM2228245 M35_30 42 12 Test No M35 30
GSM2228246 M20_30 42 12 Test No M20 30
GSM2228247 M36_30 42 12 Test No M36 30
GSM2228248 M21_7 42 12 Test No M21 7
GSM2228249 M26_Pre1 42 12 Test Yes M26 0
GSM2228250 M27_30 42 12 Test No M27 30
GSM2228251 M22_56 42 12 Test No M22 56
GSM2228252 M20_Pre1 42 12 Test Yes M20 0
GSM2228253 M25_42 42 12 Test No M25 42
GSM2228254 M30_90 43 12 Test No M30 90
GSM2228255 M34_180 43 12 Test Yes M34 180
GSM2228256 M29_7 43 12 Test No M29 7
GSM2228257 M27_42 43 12 Test No M27 42
GSM2228258 M21_20 43 12 Test No M21 20
GSM2228259 M28_Pre2 43 12 Test No M28 1
GSM2228260 M20_Pre2 43 12 Test No M20 1
GSM2228261 M21_150 43 12 Test No M21 150
GSM2228262 M26_10 43 12 Test No M26 10
infection.time clinical.status description description.1
GSM2227793 M5 Active M16_150 6303256020_A.AVG_Signal
GSM2227794 D3 Latent M17_3 6303256020_B.AVG_Signal
GSM2227795 Pre2 Active M8_Pre2 6303256020_C.AVG_Signal
GSM2227796 D56 Active M19_56 6303256020_D.AVG_Signal
GSM2227797 M3 Active M19_90 6303256020_E.AVG_Signal
GSM2227798 D7 Active M1_7 6303256020_F.AVG_Signal
GSM2227799 D20 Active M18_20 6303256020_G.AVG_Signal
GSM2227800 M3 Latent M15_90 6303256020_H.AVG_Signal
GSM2227801 D42 Active M1_42 6303256020_I.AVG_Signal
GSM2227802 M6 Latent M13_180 6303256020_J.AVG_Signal
GSM2227803 D10 Latent M17_10 6303256020_K.AVG_Signal
GSM2227804 D10 Active M18_10 6303256020_L.AVG_Signal
GSM2227805 M4 Active M6_120 6303256032_A.AVG_Signal
GSM2227806 M3 Latent M14_90 6303256032_B.AVG_Signal
GSM2227807 D20 Latent M15_20 6303256032_C.AVG_Signal
GSM2227808 M4 Latent M17_120 6303256032_D.AVG_Signal
GSM2227809 D42 Active M2_42 6303256032_E.AVG_Signal
GSM2227810 M4 Latent M9_120 6303256032_F.AVG_Signal
GSM2227811 D7 Latent M17_7 6303256032_G.AVG_Signal
GSM2227812 D30 Latent M9_30 6303256032_H.AVG_Signal
GSM2227813 M4 Active M16_120 6303256032_I.AVG_Signal
GSM2227814 M6 Latent M3_180 6303256032_J.AVG_Signal
GSM2227815 D30 Latent M3_30 6303256032_K.AVG_Signal
GSM2227816 M5 Active M19_150 6303256032_L.AVG_Signal
GSM2227817 D3 Active M19_3 6303256034_A.AVG_Signal
GSM2227818 D20 Active M1_20 6303256034_B.AVG_Signal
GSM2227819 D7 Latent M11_7 6303256034_C.AVG_Signal
GSM2227820 M4 Active M18_120 6303256034_D.AVG_Signal
GSM2227821 D3 Latent M14_3 6303256034_E.AVG_Signal
GSM2227822 D3 Latent M7_3 6303256034_F.AVG_Signal
GSM2227823 M5 Active M8_150 6303256034_G.AVG_Signal
GSM2227824 Pre2 Latent M4_Pre2 6303256034_I.AVG_Signal
GSM2227825 D56 Latent M4_56 6303256034_J.AVG_Signal
GSM2227826 Pre1 Latent M17_Pre1 6303256034_K.AVG_Signal
GSM2227827 M3 Latent M9_90 6303256034_L.AVG_Signal
GSM2227828 D56 Active M1_56 6303256038_A.AVG_Signal
GSM2227829 Pre1 Active M18_Pre1 6303256038_B.AVG_Signal
GSM2227830 Pre2 Latent M13_Pre2 6303256038_C.AVG_Signal
GSM2227831 Pre2 Latent M3_Pre2 6303256038_D.AVG_Signal
GSM2227832 D20 Latent M4_20 6303256038_E.AVG_Signal
GSM2227833 M6 Active M16_180 6303256038_F.AVG_Signal
GSM2227834 D30 Active M18_30 6303256038_G.AVG_Signal
GSM2227835 D42 Active M6_42 6303256038_H.AVG_Signal
GSM2227836 D20 Active M2_20 6303256038_I.AVG_Signal
GSM2227837 D30 Latent M14_30 6303256038_J.AVG_Signal
GSM2227838 Pre2 Latent M14_Pre2 6303256038_K.AVG_Signal
GSM2227839 M5 Latent M5_150 6303256038_L.AVG_Signal
GSM2227840 Pre1 Active M8_Pre1 6303256042_A.AVG_Signal
GSM2227841 D42 Latent M13_42 6303256042_B.AVG_Signal
GSM2227842 D30 Active M19_30 6303256042_C.AVG_Signal
GSM2227843 M5 Active M12_150 6303256042_D.AVG_Signal
GSM2227844 D56 Latent M7_56 6303256042_E.AVG_Signal
GSM2227845 D3 Latent M15_3 6303256042_F.AVG_Signal
GSM2227846 D7 Latent M15_7 6303256042_G.AVG_Signal
GSM2227847 M6 Active M1_180 6303256042_H.AVG_Signal
GSM2227848 D42 Latent M5_42 6303256042_I.AVG_Signal
GSM2227849 Pre1 Latent M3_Pre1 6303256042_J.AVG_Signal
GSM2227850 D7 Latent M13_7 6303256042_K.AVG_Signal
GSM2227851 D3 Latent M4_3 6303256042_L.AVG_Signal
GSM2227852 D10 Latent M14_10 6303281024_A.AVG_Signal
GSM2227853 Pre1 Active M10_Pre1 6303281024_B.AVG_Signal
GSM2227854 D10 Latent M5_10 6303281024_C.AVG_Signal
GSM2227855 M5 Active M2_150 6303281024_D.AVG_Signal
GSM2227856 M5 Latent M3_150 6303281024_E.AVG_Signal
GSM2227857 M4 Active M12_120 6303281024_F.AVG_Signal
GSM2227858 D42 Latent M7_42 6303281024_G.AVG_Signal
GSM2227859 D30 Active M16_30 6303281024_H.AVG_Signal
GSM2227860 D3 Active M2_3 6303281024_I.AVG_Signal
GSM2227861 M4 Active M19_120 6303281024_J.AVG_Signal
GSM2227862 D7 Latent M5_7 6303281024_K.AVG_Signal
GSM2227863 D10 Latent M4_10 6303281024_L.AVG_Signal
GSM2227864 M3 Latent M13_90 7196763028_A.AVG_Signal
GSM2227865 M4 Latent M3_120 7196763028_B.AVG_Signal
GSM2227866 M3 Active M18_90 7196763028_C.AVG_Signal
GSM2227867 D7 Active M12_7 7196763028_D.AVG_Signal
GSM2227868 M5 Latent M17_150 7196763028_E.AVG_Signal
GSM2227869 M5 Latent M4_150 7196763028_F.AVG_Signal
GSM2227870 Pre1 Active M6_Pre1 7196763028_G.AVG_Signal
GSM2227871 M5 Latent M13_150 7196763028_H.AVG_Signal
GSM2227872 M4 Latent M15_120 7196763028_I.AVG_Signal
GSM2227873 D56 Active M18_56 7196763028_J.AVG_Signal
GSM2227874 Pre1 Latent M4_Pre1 7196763028_K.AVG_Signal
GSM2227875 D42 Latent M9_42 7196763028_L.AVG_Signal
GSM2227876 D10 Latent M9_10 7196763044_A.AVG_Signal
GSM2227877 M5 Active M6_150 7196763044_B.AVG_Signal
GSM2227878 M4 Active M8_120 7196763044_C.AVG_Signal
GSM2227879 D20 Latent M14_20 7196763044_D.AVG_Signal
GSM2227880 D20 Latent M9_20 7196763044_F.AVG_Signal
GSM2227881 D7 Latent M3_7 7196763044_G.AVG_Signal
GSM2227882 Pre2 Active M18_Pre2 7196763044_H.AVG_Signal
GSM2227883 D20 Latent M3_20 7196763044_I.AVG_Signal
GSM2227884 M3 Active M10_90 7196763044_J.AVG_Signal
GSM2227885 Pre1 Active M1_Pre1 7196763044_K.AVG_Signal
GSM2227886 M3 Active M16_90 7196763044_L.AVG_Signal
GSM2227887 Pre1 Active M12_Pre1 7196763048_A.AVG_Signal
GSM2227888 D7 Active M18_7 7196763048_B.AVG_Signal
GSM2227889 M3 Latent M5_90 7196763048_C.AVG_Signal
GSM2227890 D56 Latent M11_56 7196763048_D.AVG_Signal
GSM2227891 D7 Active M10_7 7196763048_E.AVG_Signal
GSM2227892 D3 Active M8_3 7196763048_F.AVG_Signal
GSM2227893 M6 Latent M7_180 7196763048_G.AVG_Signal
GSM2227894 D30 Active M6_30 7196763048_H.AVG_Signal
GSM2227895 D56 Latent M13_56 7196763048_I.AVG_Signal
GSM2227896 Pre1 Latent M7_Pre1 7196763048_J.AVG_Signal
GSM2227897 Pre2 Active M1_Pre2 7196763048_K.AVG_Signal
GSM2227898 D56 Active M2_56 7196763048_L.AVG_Signal
GSM2227899 D30 Active M8_30 7196763054_A.AVG_Signal
GSM2227900 M5 Latent M15_150 7196763054_B.AVG_Signal
GSM2227901 D3 Latent M5_3 7196763054_C.AVG_Signal
GSM2227902 D10 Latent M7_10 7196763054_D.AVG_Signal
GSM2227903 D30 Latent M7_30 7196763054_E.AVG_Signal
GSM2227904 D7 Latent M7_7 7196763054_F.AVG_Signal
GSM2227905 M3 Active M6_90 7196763054_G.AVG_Signal
GSM2227906 D10 Latent M13_10 7196763054_H.AVG_Signal
GSM2227907 Pre1 Active M16_Pre1 7196763054_I.AVG_Signal
GSM2227908 M4 Latent M11_120 7196763054_J.AVG_Signal
GSM2227909 M5 Latent M14_150 7196763054_K.AVG_Signal
GSM2227910 D3 Latent M13_3 7196763054_L.AVG_Signal
GSM2227911 Pre2 Active M16_Pre2 7196763056_A.AVG_Signal
GSM2227912 M6 Active M12_180 7196763056_C.AVG_Signal
GSM2227913 M4 Latent M13_120 7196763056_D.AVG_Signal
GSM2227914 M3 Active M1_90 7196763056_E.AVG_Signal
GSM2227915 D42 Active M12_42 7196763056_F.AVG_Signal
GSM2227916 D3 Latent M3_3 7196763056_G.AVG_Signal
GSM2227917 D42 Latent M15_42 7196763056_H.AVG_Signal
GSM2227918 M6 Latent M5_180 7196763056_I.AVG_Signal
GSM2227919 M3 Latent M7_90 7196763056_J.AVG_Signal
GSM2227920 D10 Latent M15_10 7196763056_K.AVG_Signal
GSM2227921 Pre2 Active M2_Pre2 7196763056_L.AVG_Signal
GSM2227922 Pre2 Latent M7_Pre2 7196763057_A.AVG_Signal
GSM2227923 D56 Latent M5_56 7196763057_B.AVG_Signal
GSM2227924 D20 Active M6_20 7196763057_C.AVG_Signal
GSM2227925 D30 Active M10_30 7196763057_D.AVG_Signal
GSM2227926 M6 Latent M9_180 7196763057_E.AVG_Signal
GSM2227927 D3 Active M6_3 7196763057_F.AVG_Signal
GSM2227928 D56 Active M10_56 7196763057_G.AVG_Signal
GSM2227929 D10 Active M16_10 7196763057_H.AVG_Signal
GSM2227930 D10 Active M10_10 7196763057_I.AVG_Signal
GSM2227931 D3 Active M10_3 7196763057_J.AVG_Signal
GSM2227932 Pre2 Latent M15_Pre2 7196763057_K.AVG_Signal
GSM2227933 D56 Latent M9_56 7196763057_L.AVG_Signal
GSM2227934 D30 Latent M15_30 7196763059_A.AVG_Signal
GSM2227935 M3 Latent M3_90 7196763059_B.AVG_Signal
GSM2227936 D7 Latent M4_7 7196763059_C.AVG_Signal
GSM2227937 D3 Active M12_3 7196763059_D.AVG_Signal
GSM2227938 D7 Active M19_7 7196763059_E.AVG_Signal
GSM2227939 D20 Latent M7_20 7196763059_F.AVG_Signal
GSM2227940 D7 Latent M14_7 7196763059_G.AVG_Signal
GSM2227941 Pre1 Latent M13_Pre1 7196763059_H.AVG_Signal
GSM2227942 D10 Latent M11_10 7196763059_I.AVG_Signal
GSM2227943 D30 Latent M13_30 7196763059_J.AVG_Signal
GSM2227944 D56 Latent M17_56 7196763059_K.AVG_Signal
GSM2227945 D56 Active M16_56 7196763059_L.AVG_Signal
GSM2227946 D7 Latent M9_7 7196763068_A.AVG_Signal
GSM2227947 M5 Active M1_150 7196763068_B.AVG_Signal
GSM2227948 D3 Active M1_3 7196763068_C.AVG_Signal
GSM2227949 D20 Latent M5_20 7196763068_D.AVG_Signal
GSM2227950 D20 Active M12_20 7196763068_E.AVG_Signal
GSM2227951 D42 Latent M14_42 7196763068_F.AVG_Signal
GSM2227952 D20 Active M8_20 7196763068_G.AVG_Signal
GSM2227953 D10 Active M8_10 7196763068_H.AVG_Signal
GSM2227954 D42 Active M19_42 7196763068_I.AVG_Signal
GSM2227955 D42 Latent M4_42 7196763068_J.AVG_Signal
GSM2227956 D20 Active M19_20 7196763068_K.AVG_Signal
GSM2227957 D56 Latent M15_56 7196763068_L.AVG_Signal
GSM2227958 D42 Active M18_42 7196763078_A.AVG_Signal
GSM2227959 D7 Active M6_7 7196763078_B.AVG_Signal
GSM2227960 M6 Active M2_180 7196763078_C.AVG_Signal
GSM2227961 D10 Active M19_10 7196763078_D.AVG_Signal
GSM2227962 D30 Latent M4_30 7196763078_E.AVG_Signal
GSM2227963 D42 Active M16_42 7196763078_F.AVG_Signal
GSM2227964 D42 Latent M3_42 7196763078_G.AVG_Signal
GSM2227965 M5 Latent M9_150 7196763078_H.AVG_Signal
GSM2227966 Pre1 Latent M11_Pre1 7196763078_I.AVG_Signal
GSM2227967 D3 Latent M11_3 7196763078_J.AVG_Signal
GSM2227968 D56 Active M8_56 7196763078_K.AVG_Signal
GSM2227969 M6 Latent M17_180 7196763078_L.AVG_Signal
GSM2227970 M5 Active M18_150 7196763081_A.AVG_Signal
GSM2227971 M3 Latent M4_90 7196763081_B.AVG_Signal
GSM2227972 Pre1 Latent M15_Pre1 7196763081_C.AVG_Signal
GSM2227973 M6 Latent M15_180 7196763081_D.AVG_Signal
GSM2227974 D30 Active M1_30 7196763081_E.AVG_Signal
GSM2227975 M5 Latent M11_150 7196763081_F.AVG_Signal
GSM2227976 M4 Latent M4_120 7196763081_G.AVG_Signal
GSM2227977 D7 Active M16_7 7196763081_H.AVG_Signal
GSM2227978 D42 Active M8_42 7196763081_I.AVG_Signal
GSM2227979 D10 Active M12_10 7196763081_J.AVG_Signal
GSM2227980 M3 Active M2_90 7196763081_K.AVG_Signal
GSM2227981 D7 Active M2_7 7196763081_L.AVG_Signal
GSM2227982 D10 Active M1_10 7196763087_A.AVG_Signal
GSM2227983 D42 Latent M11_42 7196763087_B.AVG_Signal
GSM2227984 M5 Latent M7_150 7196763087_C.AVG_Signal
GSM2227985 D56 Latent M14_56 7196763087_D.AVG_Signal
GSM2227986 D20 Active M10_20 7196763087_E.AVG_Signal
GSM2227987 M6 Latent M11_180 7196763087_F.AVG_Signal
GSM2227988 D30 Latent M5_30 7196763087_G.AVG_Signal
GSM2227989 Pre2 Active M12_Pre2 7196763087_H.AVG_Signal
GSM2227990 Pre1 Active M2_Pre1 7196763087_I.AVG_Signal
GSM2227991 Pre1 Latent M9_Pre1 7196763087_J.AVG_Signal
GSM2227992 Pre2 Active M6_Pre2 7196763087_K.AVG_Signal
GSM2227993 M4 Active M1_120 7196763087_L.AVG_Signal
GSM2227994 Pre2 Latent M5_Pre2 7196771004_A.AVG_Signal
GSM2227995 D10 Active M2_10 7196771004_B.AVG_Signal
GSM2227996 D3 Latent M9_3 7196771004_C.AVG_Signal
GSM2227997 M4 Latent M7_120 7196771004_D.AVG_Signal
GSM2227998 Pre1 Latent M5_Pre1 7196771004_E.AVG_Signal
GSM2227999 D30 Active M2_30 7196771004_F.AVG_Signal
GSM2228000 D3 Active M16_3 7196771004_G.AVG_Signal
GSM2228001 D30 Latent M11_30 7196771004_H.AVG_Signal
GSM2228002 D42 Active M10_42 7196771004_I.AVG_Signal
GSM2228003 D30 Active M12_30 7196771004_J.AVG_Signal
GSM2228004 D56 Active M12_56 7196771004_K.AVG_Signal
GSM2228005 M3 Latent M17_90 7196771004_L.AVG_Signal
GSM2228006 M4 Latent M14_120 7196771005_A.AVG_Signal
GSM2228007 D7 Active M8_7 7196771005_B.AVG_Signal
GSM2228008 Pre1 Latent M14_Pre1 7196771005_C.AVG_Signal
GSM2228009 M3 Active M12_90 7196771005_D.AVG_Signal
GSM2228010 M3 Active M8_90 7196771005_E.AVG_Signal
GSM2228011 M6 Active M8_180 7196771005_G.AVG_Signal
GSM2228012 M6 Latent M4_180 7196771005_H.AVG_Signal
GSM2228013 D10 Latent M3_10 7196771005_I.AVG_Signal
GSM2228014 Pre2 Active M10_Pre2 7196771005_J.AVG_Signal
GSM2228015 M6 Active M6_180 7196771005_K.AVG_Signal
GSM2228016 D56 Latent M3_56 7196771005_L.AVG_Signal
GSM2228017 M4 Latent M5_120 7196771011_A.AVG_Signal
GSM2228018 D30 Latent M17_30 7196771011_B.AVG_Signal
GSM2228019 D20 Active M16_20 7196771011_C.AVG_Signal
GSM2228020 D20 Latent M11_20 7196771011_D.AVG_Signal
GSM2228021 D42 Latent M17_42 7196771011_E.AVG_Signal
GSM2228022 M6 Latent M14_180 7196771011_F.AVG_Signal
GSM2228023 D20 Latent M13_20 7196771011_G.AVG_Signal
GSM2228024 M3 Latent M11_90 7196771011_H.AVG_Signal
GSM2228025 D20 Latent M17_20 7196771011_I.AVG_Signal
GSM2228026 D56 Active M6_56 7196771011_J.AVG_Signal
GSM2228027 M4 Active M2_120 7196771011_K.AVG_Signal
GSM2228028 Pre1 Active M19_Pre1 7196771011_L.AVG_Signal
GSM2228029 D10 Active M6_10 7196798073_J.AVG_Signal
GSM2228030 D3 Active M18_3 7196798073_K.AVG_Signal
GSM2228031 Pre1 Active M24_Pre1 9248833066_A.AVG_Signal
GSM2228032 M4 Active M32_120 9248833066_B.AVG_Signal
GSM2228033 D10 Latent M36_10 9248833066_D.AVG_Signal
GSM2228034 D56 Latent M20_56 9248833066_E.AVG_Signal
GSM2228035 M3 Active M23_90 9248833066_H.AVG_Signal
GSM2228036 M3 Active M24_90 9248833066_I.AVG_Signal
GSM2228037 D42 Latent M34_42 9248833066_J.AVG_Signal
GSM2228038 M3 Latent M29_90 9248833066_K.AVG_Signal
GSM2228039 D10 Latent M28_10 9248833068_A.AVG_Signal
GSM2228040 D7 Latent M33_7 9248833068_B.AVG_Signal
GSM2228041 M6 Latent M28_180 9248833068_C.AVG_Signal
GSM2228042 D10 Active M31_10 9248833068_D.AVG_Signal
GSM2228043 D3 Latent M33_3 9248833068_E.AVG_Signal
GSM2228044 D20 Latent M29_20 9248833068_F.AVG_Signal
GSM2228045 Pre1 Active M23_Pre1 9248833068_G.AVG_Signal
GSM2228046 M6 Latent M20_180 9248833068_H.AVG_Signal
GSM2228047 M5 Latent M28_150 9248833068_I.AVG_Signal
GSM2228048 Pre2 Latent M25_Pre2 9248833068_L.AVG_Signal
GSM2228049 M5 Latent M33_150 9248833069_A.AVG_Signal
GSM2228050 M5 Latent M20_150 9248833069_B.AVG_Signal
GSM2228051 M5 Active M22_150 9248833069_C.AVG_Signal
GSM2228052 D7 Active M23_7 9248833069_D.AVG_Signal
GSM2228053 D42 Latent M36_42 9248833069_E.AVG_Signal
GSM2228054 D42 Active M23_42 9248833069_F.AVG_Signal
GSM2228055 M5 Latent M29_150 9248833069_G.AVG_Signal
GSM2228056 D30 Latent M34_30 9248833069_H.AVG_Signal
GSM2228057 D56 Latent M26_56 9248833069_I.AVG_Signal
GSM2228058 D56 Active M30_56 9248833069_J.AVG_Signal
GSM2228059 M3 Latent M27_90 9248833069_K.AVG_Signal
GSM2228060 D10 Latent M27_10 9248833069_L.AVG_Signal
GSM2228061 D10 Active M22_10 9248833070_A.AVG_Signal
GSM2228062 D42 Latent M29_42 9248833070_C.AVG_Signal
GSM2228063 D3 Active M38_3 9248833070_D.AVG_Signal
GSM2228064 Pre1 Latent M28_Pre1 9248833070_E.AVG_Signal
GSM2228065 M3 Active M38_90 9248833070_F.AVG_Signal
GSM2228066 M6 Latent M21_180 9248833070_G.AVG_Signal
GSM2228067 Pre1 Latent M29_Pre1 9248833070_H.AVG_Signal
GSM2228068 D3 Latent M34_3 9248833070_I.AVG_Signal
GSM2228069 M3 Latent M25_90 9248833070_J.AVG_Signal
GSM2228070 D30 Active M22_30 9248833070_K.AVG_Signal
GSM2228071 Pre1 Latent M25_Pre1 9248833070_L.AVG_Signal
GSM2228072 D10 Latent M20_10 9248833072_A.AVG_Signal
GSM2228073 M6 Latent M29_180 9248833072_B.AVG_Signal
GSM2228074 D56 Active M31_56 9248833072_C.AVG_Signal
GSM2228075 D20 Latent M27_20 9248833072_D.AVG_Signal
GSM2228076 D10 Active M23_10 9248833072_E.AVG_Signal
GSM2228077 M6 Active M30_180 9248833072_F.AVG_Signal
GSM2228078 D42 Latent M26_42 9248833072_G.AVG_Signal
GSM2228079 Pre2 Active M23_Pre2 9248833072_H.AVG_Signal
GSM2228080 D56 Latent M35_56 9248833072_I.AVG_Signal
GSM2228081 Pre2 Latent M26_Pre2 9248833072_J.AVG_Signal
GSM2228082 D20 Latent M36_20 9248833072_K.AVG_Signal
GSM2228083 D3 Active M30_3 9248833073_A.AVG_Signal
GSM2228084 M3 Latent M28_90 9248833073_B.AVG_Signal
GSM2228085 D42 Active M30_42 9248833073_C.AVG_Signal
GSM2228086 D10 Latent M34_10 9248833073_D.AVG_Signal
GSM2228087 Pre2 Latent M37_Pre2 9248833073_G.AVG_Signal
GSM2228088 M6 Latent M37_180 9248833073_H.AVG_Signal
GSM2228089 D56 Latent M33_56 9248833073_I.AVG_Signal
GSM2228090 M3 Latent M20_90 9248833073_J.AVG_Signal
GSM2228091 M6 Latent M36_180 9248833073_K.AVG_Signal
GSM2228092 D30 Latent M25_30 9248833073_L.AVG_Signal
GSM2228093 D56 Active M23_56 9248833074_A.AVG_Signal
GSM2228094 D42 Latent M21_42 9248833074_C.AVG_Signal
GSM2228095 M3 Latent M26_90 9248833074_F.AVG_Signal
GSM2228096 D30 Latent M21_30 9248833074_G.AVG_Signal
GSM2228097 D20 Latent M34_20 9248833074_H.AVG_Signal
GSM2228098 M6 Active M22_180 9248833074_I.AVG_Signal
GSM2228099 D56 Latent M21_56 9248833074_J.AVG_Signal
GSM2228100 D30 Active M32_30 9248833074_K.AVG_Signal
GSM2228101 Pre1 Latent M35_Pre1 9248833074_L.AVG_Signal
GSM2228102 M6 Latent M26_180 9248833075_A.AVG_Signal
GSM2228103 Pre2 Active M32_Pre2 9248833075_B.AVG_Signal
GSM2228104 M3 Latent M37_90 9248833075_C.AVG_Signal
GSM2228105 M6 Latent M27_180 9248833075_D.AVG_Signal
GSM2228106 M4 Active M38_120 9248833075_E.AVG_Signal
GSM2228107 Pre2 Active M31_Pre2 9248833075_F.AVG_Signal
GSM2228108 D20 Active M30_20 9248833075_G.AVG_Signal
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In [28]:
PAL.5 = get.monkey.expressed.genes(raw.expres)
[1] "Genes expressed in at least 5 % of samples: 15083"
In [35]:
expres = process.monkey.exprs(exprs(monkey.gset), PAL.5)
Batch correct expression data by hybridization chamber and dataset as in Gideon et al. 2016¶
In [39]:
modcombat<-model.matrix(~1, data=pheno)
combat_dataset= ComBat(dat=expres, batch=pheno$dataset, mod=modcombat, par.prior=TRUE, prior.plots=TRUE)
expres = ComBat(dat=combat_dataset, batch=pheno$hyb.chamber, mod=modcombat, par.prior=TRUE, prior.plots=TRUE)
Found2batches Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors Finding parametric adjustments Adjusting the Data Found12batches Adjusting for0covariate(s) or covariate level(s)
Standardizing Data across genes
Fitting L/S model and finding priors Finding parametric adjustments Adjusting the Data
Statistically Filter the genes by 60% most variable genes¶
In [40]:
cut = 0.6
filter = function(x) {IQR(x) / median(x)}
genes.range = apply(expres, 1, filter)
criteria = quantile(genes.range, c(1-cut))
genes.f = rownames(expres)[genes.range > criteria]
expres = expres[genes.f,]
Focus on time points 20 - 180 days, and store a copy of the pheno table¶
In [46]:
expres = expres[, pheno$time.point > 10]
pheno = pheno[pheno$time.point > 10,]
In [51]:
pheno$time.period = as.factor(ifelse(pheno$time.point >= 90,
"late",
"early"))
In [53]:
write.table(pheno, file = paste(monkey.path, "PhenoData_processed.txt", sep="/"),sep="\t")
In [55]:
pheno = read.table(file = paste(monkey.path, "PhenoData_processed.txt", sep="/"),sep="\t")
In [57]:
pheno
| title | ChIP | hyb.chamber | dataset | synchroset | monkeyid | time.point | infection.time | clinical.status | description | description.1 | time.point.comb | time.period | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| GSM2227793 | M16_150 | 1 | 1 | Training | No | M16 | 150 | M5 | Active | M16_150 | 6303256020_A.AVG_Signal | 150 | late |
| GSM2227796 | M19_56 | 1 | 1 | Training | No | M19 | 56 | D56 | Active | M19_56 | 6303256020_D.AVG_Signal | 56 | early |
| GSM2227797 | M19_90 | 1 | 1 | Training | No | M19 | 90 | M3 | Active | M19_90 | 6303256020_E.AVG_Signal | 90 | late |
| GSM2227799 | M18_20 | 1 | 1 | Training | No | M18 | 20 | D20 | Active | M18_20 | 6303256020_G.AVG_Signal | 20 | early |
| GSM2227800 | M15_90 | 1 | 1 | Training | No | M15 | 90 | M3 | Latent | M15_90 | 6303256020_H.AVG_Signal | 90 | late |
| GSM2227801 | M1_42 | 1 | 1 | Training | No | M1 | 42 | D42 | Active | M1_42 | 6303256020_I.AVG_Signal | 42 | early |
| GSM2227802 | M13_180 | 1 | 1 | Training | Yes | M13 | 180 | M6 | Latent | M13_180 | 6303256020_J.AVG_Signal | 180 | late |
| GSM2227805 | M6_120 | 2 | 1 | Training | No | M6 | 120 | M4 | Active | M6_120 | 6303256032_A.AVG_Signal | 120 | late |
| GSM2227806 | M14_90 | 2 | 1 | Training | No | M14 | 90 | M3 | Latent | M14_90 | 6303256032_B.AVG_Signal | 90 | late |
| GSM2227807 | M15_20 | 2 | 1 | Training | No | M15 | 20 | D20 | Latent | M15_20 | 6303256032_C.AVG_Signal | 20 | early |
| GSM2227808 | M17_120 | 2 | 1 | Training | No | M17 | 120 | M4 | Latent | M17_120 | 6303256032_D.AVG_Signal | 120 | late |
| GSM2227809 | M2_42 | 2 | 1 | Training | No | M2 | 42 | D42 | Active | M2_42 | 6303256032_E.AVG_Signal | 42 | early |
| GSM2227810 | M9_120 | 2 | 1 | Training | No | M9 | 120 | M4 | Latent | M9_120 | 6303256032_F.AVG_Signal | 120 | late |
| GSM2227812 | M9_30 | 2 | 1 | Training | No | M9 | 30 | D30 | Latent | M9_30 | 6303256032_H.AVG_Signal | 30 | early |
| GSM2227813 | M16_120 | 2 | 1 | Training | No | M16 | 120 | M4 | Active | M16_120 | 6303256032_I.AVG_Signal | 120 | late |
| GSM2227814 | M3_180 | 2 | 1 | Training | Yes | M3 | 180 | M6 | Latent | M3_180 | 6303256032_J.AVG_Signal | 180 | late |
| GSM2227815 | M3_30 | 2 | 1 | Training | No | M3 | 30 | D30 | Latent | M3_30 | 6303256032_K.AVG_Signal | 30 | early |
| GSM2227816 | M19_150 | 2 | 1 | Training | Yes | M19 | 150 | M5 | Active | M19_150 | 6303256032_L.AVG_Signal | 150 | late |
| GSM2227818 | M1_20 | 3 | 1 | Training | No | M1 | 20 | D20 | Active | M1_20 | 6303256034_B.AVG_Signal | 20 | early |
| GSM2227820 | M18_120 | 3 | 1 | Training | No | M18 | 120 | M4 | Active | M18_120 | 6303256034_D.AVG_Signal | 120 | late |
| GSM2227823 | M8_150 | 3 | 1 | Training | No | M8 | 150 | M5 | Active | M8_150 | 6303256034_G.AVG_Signal | 150 | late |
| GSM2227825 | M4_56 | 3 | 1 | Training | No | M4 | 56 | D56 | Latent | M4_56 | 6303256034_J.AVG_Signal | 56 | early |
| GSM2227827 | M9_90 | 3 | 1 | Training | No | M9 | 90 | M3 | Latent | M9_90 | 6303256034_L.AVG_Signal | 90 | late |
| GSM2227828 | M1_56 | 4 | 1 | Training | No | M1 | 56 | D56 | Active | M1_56 | 6303256038_A.AVG_Signal | 56 | early |
| GSM2227832 | M4_20 | 4 | 1 | Training | No | M4 | 20 | D20 | Latent | M4_20 | 6303256038_E.AVG_Signal | 20 | early |
| GSM2227833 | M16_180 | 4 | 1 | Training | Yes | M16 | 180 | M6 | Active | M16_180 | 6303256038_F.AVG_Signal | 180 | late |
| GSM2227834 | M18_30 | 4 | 1 | Training | No | M18 | 30 | D30 | Active | M18_30 | 6303256038_G.AVG_Signal | 30 | early |
| GSM2227835 | M6_42 | 4 | 1 | Training | No | M6 | 42 | D42 | Active | M6_42 | 6303256038_H.AVG_Signal | 42 | early |
| GSM2227836 | M2_20 | 4 | 1 | Training | No | M2 | 20 | D20 | Active | M2_20 | 6303256038_I.AVG_Signal | 20 | early |
| GSM2227837 | M14_30 | 4 | 1 | Training | No | M14 | 30 | D30 | Latent | M14_30 | 6303256038_J.AVG_Signal | 30 | early |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
| GSM2228211 | M20_42 | 39 | 11 | Test | No | M20 | 42 | D42 | Latent | M20_42 | 9248833091_B.AVG_Signal | 42 | early |
| GSM2228212 | M37_120 | 39 | 11 | Test | No | M37 | 120 | M4 | Latent | M37_120 | 9248833091_C.AVG_Signal | 120 | late |
| GSM2228215 | M22_20 | 39 | 11 | Test | No | M22 | 20 | D20 | Active | M22_20 | 9248833091_F.AVG_Signal | 20 | early |
| GSM2228218 | M22_42 | 39 | 11 | Test | No | M22 | 42 | D42 | Active | M22_42 | 9248833091_I.AVG_Signal | 42 | early |
| GSM2228223 | M34_90 | 40 | 11 | Test | No | M34 | 90 | M3 | Latent | M34_90 | 9248833092_B.AVG_Signal | 90 | late |
| GSM2228224 | M33_30 | 40 | 11 | Test | No | M33 | 30 | D30 | Latent | M33_30 | 9248833092_C.AVG_Signal | 30 | early |
| GSM2228225 | M26_20 | 40 | 11 | Test | No | M26 | 20 | D20 | Latent | M26_20 | 9248833092_D.AVG_Signal | 20 | early |
| GSM2228228 | M20_20 | 40 | 11 | Test | No | M20 | 20 | D20 | Latent | M20_20 | 9248833092_G.AVG_Signal | 20 | early |
| GSM2228229 | M33_90 | 40 | 11 | Test | No | M33 | 90 | M3 | Latent | M33_90 | 9248833092_H.AVG_Signal | 90 | late |
| GSM2228230 | M24_150 | 40 | 11 | Test | No | M24 | 150 | M5 | Active | M24_150 | 9248833092_I.AVG_Signal | 150 | late |
| GSM2228231 | M27_150 | 40 | 11 | Test | No | M27 | 150 | M5 | Latent | M27_150 | 9248833092_J.AVG_Signal | 150 | late |
| GSM2228232 | M38_30 | 40 | 11 | Test | No | M38 | 30 | D30 | Active | M38_30 | 9248833092_K.AVG_Signal | 30 | early |
| GSM2228237 | M35_180 | 41 | 11 | Test | Yes | M35 | 180 | M6 | Latent | M35_180 | 9248833093_F.AVG_Signal | 180 | late |
| GSM2228238 | M26_30 | 41 | 11 | Test | No | M26 | 30 | D30 | Latent | M26_30 | 9248833093_G.AVG_Signal | 30 | early |
| GSM2228239 | M22_120 | 41 | 11 | Test | No | M22 | 120 | M4 | Active | M22_120 | 9248833093_H.AVG_Signal | 120 | late |
| GSM2228240 | M32_150 | 41 | 11 | Test | No | M32 | 150 | M5 | Active | M32_150 | 9248833093_I.AVG_Signal | 150 | late |
| GSM2228242 | M24_20 | 41 | 11 | Test | No | M24 | 20 | D20 | Active | M24_20 | 9248833093_K.AVG_Signal | 20 | early |
| GSM2228243 | M33_20 | 41 | 11 | Test | No | M33 | 20 | D20 | Latent | M33_20 | 9248833093_L.AVG_Signal | 20 | early |
| GSM2228244 | M28_30 | 42 | 12 | Test | No | M28 | 30 | D30 | Latent | M28_30 | 9248833095_A.AVG_Signal | 30 | early |
| GSM2228245 | M35_30 | 42 | 12 | Test | No | M35 | 30 | D30 | Latent | M35_30 | 9248833095_B.AVG_Signal | 30 | early |
| GSM2228246 | M20_30 | 42 | 12 | Test | No | M20 | 30 | D30 | Latent | M20_30 | 9248833095_C.AVG_Signal | 30 | early |
| GSM2228247 | M36_30 | 42 | 12 | Test | No | M36 | 30 | D30 | Latent | M36_30 | 9248833095_D.AVG_Signal | 30 | early |
| GSM2228250 | M27_30 | 42 | 12 | Test | No | M27 | 30 | D30 | Latent | M27_30 | 9248833095_I.AVG_Signal | 30 | early |
| GSM2228251 | M22_56 | 42 | 12 | Test | No | M22 | 56 | D56 | Active | M22_56 | 9248833095_J.AVG_Signal | 56 | early |
| GSM2228253 | M25_42 | 42 | 12 | Test | No | M25 | 42 | D42 | Latent | M25_42 | 9248833095_L.AVG_Signal | 42 | early |
| GSM2228254 | M30_90 | 43 | 12 | Test | No | M30 | 90 | M3 | Active | M30_90 | 9248833101_A.AVG_Signal | 90 | late |
| GSM2228255 | M34_180 | 43 | 12 | Test | Yes | M34 | 180 | M6 | Latent | M34_180 | 9248833101_B.AVG_Signal | 180 | late |
| GSM2228257 | M27_42 | 43 | 12 | Test | No | M27 | 42 | D42 | Latent | M27_42 | 9248833101_D.AVG_Signal | 42 | early |
| GSM2228258 | M21_20 | 43 | 12 | Test | No | M21 | 20 | D20 | Latent | M21_20 | 9248833101_E.AVG_Signal | 20 | early |
| GSM2228261 | M21_150 | 43 | 12 | Test | No | M21 | 150 | M5 | Latent | M21_150 | 9248833101_H.AVG_Signal | 150 | late |
Divide the data into a training and test set (70-30 split), with preserved ratio of monkeys with latent or active tuberculosis.¶
- This split was performed in python, which is located Macaque_analysis_train_test_set_split.ipynb
In [58]:
test.latent.monkeys = c('M13', 'M27', 'M34', 'M15', 'M35', 'M36')
test.active.monkeys = c('M1', 'M16', 'M32', 'M18', 'M23')
In [68]:
test.rows = rownames(pheno[pheno$monkeyid %in% union(test.latent.monkeys, test.active.monkeys),])
train.rows = setdiff(rownames(pheno), test.rows)
if (dim(expres)[1] > dim(expres)[2])
expres = t(expres)
pheno.train = droplevels(pheno[train.rows,])
pheno.test = droplevels(pheno[test.rows,])
expres.train = expres[train.rows,]
expres.test = expres[test.rows,]
Create grouped k-folds according to monkey within the training set. With random seed set to 100, this gives 9-folds¶
In [72]:
set.seed(100)
folds = groupKFold(pheno.train$monkeyid, k=10)
for (fold in lapply(folds, function(x) {pheno.train$monkeyid[x]}))
print(length((as.character(fold))))
lapply(folds, function(x, y) table(y[x]), y = pheno.train$monkeyid)
[1] 202 [1] 194 [1] 186 [1] 174 [1] 187 [1] 179 [1] 186 [1] 178 [1] 194
$Fold1 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 5 8 8 0 8 7 8 8 8 8 8 8 8 8 8 8 8 7 8 8 M38 M4 M5 M6 M7 M8 M9 7 8 8 8 8 8 8 $Fold2 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 5 8 8 8 8 7 8 8 8 0 8 8 8 8 8 8 8 7 8 8 M38 M4 M5 M6 M7 M8 M9 7 8 8 8 8 0 8 $Fold3 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 5 0 8 8 8 7 8 8 8 8 8 8 0 8 8 8 0 7 8 8 M38 M4 M5 M6 M7 M8 M9 7 8 8 8 8 8 8 $Fold4 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 0 8 8 8 8 7 8 0 8 8 8 8 8 0 8 8 8 0 0 8 M38 M4 M5 M6 M7 M8 M9 7 8 8 8 8 8 8 $Fold5 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 5 8 8 8 0 0 8 8 8 8 8 8 8 8 8 8 8 7 8 8 M38 M4 M5 M6 M7 M8 M9 7 8 8 8 0 8 8 $Fold6 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 5 8 0 8 8 7 8 8 0 8 8 8 8 8 8 8 8 7 8 8 M38 M4 M5 M6 M7 M8 M9 0 8 0 8 8 8 8 $Fold7 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 5 8 8 8 8 7 8 8 8 8 0 8 8 8 8 0 8 7 8 0 M38 M4 M5 M6 M7 M8 M9 7 8 8 8 8 8 8 $Fold8 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 5 8 8 8 8 7 8 8 8 8 8 8 8 8 0 8 8 7 8 8 M38 M4 M5 M6 M7 M8 M9 7 0 8 0 8 8 0 $Fold9 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 5 8 8 8 8 7 0 8 8 8 8 0 8 8 8 8 8 7 8 8 M38 M4 M5 M6 M7 M8 M9 7 8 8 8 8 8 8
Reproduce Supplementary Figure S2 (this takes quite a bit of time)¶
In [ ]:
seed=7
# Now we are going to loop over different models
start_time <- Sys.time()
cluster = makeCluster(detectCores()-3) # Leaving 3 for other jobs
registerDoParallel(cluster)
methods = c("gbm", "svmRadial", "svmPoly", "ranger", "glmnet")
models = list()
#folds
control <- trainControl(method="cv", index=folds, search="random", allowParallel=TRUE, savePredictions='final',
classProbs=TRUE, summaryFunction=twoClassSummary)
for (alg in methods) {
set.seed(seed)
print("I have gotten to model:")
print(alg)
model = train(expres.train, pheno.train$time.period, method=alg, tuneLength=50, trControl=control,
metric="ROC")
models[[alg]] = model
}
stopCluster(cluster)
registerDoSEQ()
end_time <- Sys.time()
print(end_time - start_time)
In [ ]:
results = resamples(models)
bwplot(results)
Reproduce Figure 2A,B, using glmnet. Do a larger hyperparameter search to find the optimally performing model on the training set.¶
In [76]:
set.seed(100)
n = 10000
lambda.grid = c(10 ^ runif(n, min = log10(1e-5), max = log10(1e2)))
alpha.grid = runif(length(lambda.grid), min = 0.00, 1.0)
train.grid = data.frame(lambda = sample(lambda.grid, length(lambda.grid)),
alpha = sample(alpha.grid, length(lambda.grid)))
In [77]:
seed=7
start_time <- Sys.time()
cluster = makeCluster(detectCores()-3) # Leaving 3 for other jobs
registerDoParallel(cluster)
methods = c( "glmnet")
models = list()
control <- trainControl(method="cv", index=folds, allowParallel=TRUE, savePredictions='final',
classProbs=TRUE, summaryFunction=twoClassSummary)
for (alg in methods) {
set.seed(seed)
print("I have gotten to model:")
print(alg)
model = train(expres.train, pheno.train$time.period, method=alg, trControl=control, tuneGrid=train.grid,
metric="ROC")
models[[alg]] = model
}
stopCluster(cluster)
registerDoSEQ()
end_time <- Sys.time()
print(end_time - start_time)
[1] "I have gotten to model:" [1] "glmnet" Time difference of 2.033721 hours
In [81]:
glmres = models$glmnet$results
graph.hyper(glmres$alpha, log10(glmres$lambda), glmres$ROC)
Figure 2A¶
In [83]:
models.monkey.ROC = models
model.monkey.ROC = models.monkey.ROC$glmnet
glmnet.monkey.val.ROC = my.roc(model.monkey.ROC$pred$early, model.monkey.ROC$pred$obs, "early")
pred.monkey.test = predict(model.monkey.ROC, newdata = expres.test, type="prob")
glmnet.monkey.ROC = my.roc(pred.monkey.test$early,
pheno.test$time.period,
"early")
[1] "This is the AUC:" Area under the curve: 0.7835 [1] "This is the AUC p-value:" [1] 6.39348e-13 [1] "This is the AUC 95% Confidence Interval" 95% CI: 0.7222-0.8448 (DeLong) [1] "This is the AUC:" Area under the curve: 0.808 [1] "This is the AUC p-value:" [1] 2.039996e-07 [1] "This is the AUC 95% Confidence Interval" 95% CI: 0.7122-0.9037 (DeLong)
In [84]:
monkey.class.plot = ggroc(list(CV=glmnet.monkey.val.ROC, test=glmnet.monkey.ROC), legacy.axes=TRUE) +
geom_abline(intercept = 0, slope = 1, color = "lightgrey", size = 0.25) +
ggtitle("Early vs. Late Time Period") + theme(plot.title = element_text(size=12, face="plain")) +
scale_color_manual(name="M.tb Infected Macaques",
labels=c("CV" =expression("CV: AUC 0.78, p ="~5.6~ "x" ~10^{-13} ~ " "),
"test"=expression("Test: AUC 0.81, p ="~1.6~ "x" ~10^{-7} ~ " ")),
values=c("CV"="blue", "test"="red")) +
theme(legend.position=c(0.30,0.20), legend.title = element_text(size=12), legend.text = element_text(size=11))
monkey.class.plot
Figure 2B¶
In [88]:
monkey.val.preds = models.monkey.ROC$glmnet$pred
pred.monkey.test = predict(models.monkey.ROC$glmnet, newdata=expres.test)
# Calculate ratio of active to latent in all actual and predicted early and late time periods, in both validation and test set
a.l.early.val = sum(dplyr::filter(pheno.train, time.period=="early")$clinical.status == "Active") / length(dplyr::filter(pheno.train, time.period=="early")$clinical.status)
a.l.late.val = sum(dplyr::filter(pheno.train, time.period=="late")$clinical.status == "Active") / length(dplyr::filter(pheno.train, time.period=="late")$clinical.status)
a.l.p.early.val = sum(pheno.train[monkey.val.preds$pred == "early",]$clinical.status=="Active") / length(pheno.train[monkey.val.preds$pred == "early",]$clinical.status)
a.l.p.late.val = sum(pheno.train[monkey.val.preds$pred == "late",]$clinical.status=="Active") / length(pheno.train[monkey.val.preds$pred == "late",]$clinical.status)
# Calculate ratio of active to latent in all actual and predicted early and late time periods, in both validation and test set
a.l.early.test = sum(dplyr::filter(pheno.test, time.period=="early")$clinical.status == "Active") / length(dplyr::filter(pheno.test, time.period=="early")$clinical.status)
a.l.late.test = sum(dplyr::filter(pheno.test, time.period=="late")$clinical.status == "Active") / length(dplyr::filter(pheno.test, time.period=="late")$clinical.status)
a.l.p.early.test = sum(pheno.test[pred.monkey.test == "early",]$clinical.status=="Active") / length(pheno.test[pred.monkey.test == "early",]$clinical.status)
a.l.p.late.test = sum(pheno.test[pred.monkey.test == "late",]$clinical.status=="Active") / length(pheno.test[pred.monkey.test == "late",]$clinical.status)
t.period = c("Early", "Predicted Early", "Early", "Predicted Early")
partition = as.factor(c("Cross-Validation", "Cross-Validation", "Test", "Test"))
active_prop = c(a.l.early.val, a.l.p.early.val, a.l.early.test, a.l.p.early.test)
partition = factor(partition, levels(partition)[c(which(levels(partition) == "Cross-Validation"),
which(levels(partition) == "Test"))])
disease.confound = data.frame(t.period=as.factor(t.period), partition=partition, active_prop=active_prop)
n.act.early.val = sum(dplyr::filter(pheno.train, time.period=="early")$clinical.status == "Active")
n.tot.early.val = length(dplyr::filter(pheno.train, time.period=="early")$clinical.status)
n.act.p.early.val = sum(pheno.train[monkey.val.preds$pred == "early",]$clinical.status=="Active")
n.tot.p.early.val = length(pheno.train[monkey.val.preds$pred == "early",]$clinical.status)
fisher.test(matrix(c(n.act.early.val,
n.tot.early.val - n.act.early.val,
n.act.p.early.val,
n.tot.p.early.val - n.act.p.early.val), ncol=2))
n.act.early.test = sum(dplyr::filter(pheno.test, time.period=="early")$clinical.status == "Active")
n.tot.early.test = length(dplyr::filter(pheno.test, time.period=="early")$clinical.status)
n.act.p.early.test = sum(pheno.test[pred.monkey.test == "early",]$clinical.status=="Active")
n.tot.p.early.test = length(pheno.test[pred.monkey.test == "early",]$clinical.status)
fisher.test(matrix(c(n.act.early.test,
n.tot.early.test - n.act.early.test,
n.act.p.early.test,
n.tot.p.early.test - n.act.p.early.test), ncol=2))
Fisher's Exact Test for Count Data data: p-value = 0.8893 alternative hypothesis: true odds ratio is not equal to 1 95 percent confidence interval: 0.5304016 1.7128232 sample estimates: odds ratio 0.9533132
Fisher's Exact Test for Count Data data: p-value = 1 alternative hypothesis: true odds ratio is not equal to 1 95 percent confidence interval: 0.4008563 2.3838605 sample estimates: odds ratio 0.9784834
In [92]:
a.l.plot = ggplot(disease.confound, aes(x=t.period, y=active_prop)) + geom_bar(stat="identity") + facet_grid(~partition) +
labs(
y="Proportion of Samples from\nMacaques with Active TB") +
ggtitle("Risk of TB", subtitle="Stratified by Predicted Time Period") + theme(plot.title = element_text(size=12, face="plain", hjust=0.5),
plot.subtitle=element_text(size=11, face="plain", hjust=0.5)) +
ylim(0, 1.0) +
geom_signif(comparisons=list(c("Early", "Predicted Early")),
y_position=c(0.75),
annotations = c("1.0"), vjust=-0.5,
tip_length=0) + theme(panel.background = element_rect(fill = "white", colour = "white", size = 4)) +
scale_x_discrete(labels=c("Early" = "Early", "Predicted Early"="Predicted\nEarly")) +
theme(axis.title.x=element_blank())
In [93]:
a.l.plot
Reproduce Figure 2C-F, using glmnet for regression. Do a hyperparameter search to find the optimally performing model on the training set.¶
In [94]:
set.seed(100)
n = 1000
lambda.grid = c(10 ^ runif(n, min = log10(1e0), max = log10(1e2)))
alpha.grid = runif(length(lambda.grid), min = 0.00, 1.00)
train.grid = data.frame(lambda = sample(lambda.grid, length(lambda.grid)),
alpha = sample(alpha.grid, length(lambda.grid)))
In [95]:
seed=7
library(doParallel)
start_time <- Sys.time()
cluster = makeCluster(detectCores()-3) # Leaving 3 for other jobs
registerDoParallel(cluster)
methods = c("glmnet")
models = list()
#folds
control <- trainControl(method="cv", index=folds, savePredictions = 'final', allowParallel=TRUE)
for (alg in methods) {
set.seed(seed)
print("I have gotten to model:")
print(alg)
model = train(expres.train, pheno.train$time.point, tuneGrid = train.grid, method=alg, trControl=control) #
models[[alg]] = model
}
stopCluster(cluster)
registerDoSEQ()
end_time <- Sys.time()
print(end_time - start_time)
[1] "I have gotten to model:" [1] "glmnet"
Warning message in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : “There were missing values in resampled performance measures.”
Time difference of 11.28674 mins
In [96]:
glmres = models$glmnet$results
graph.hyper(glmres$alpha, log10(glmres$lambda), glmres$MAE)
In [97]:
regress.model = models
In [99]:
val.regress.model = regress.model
val.pred = val.regress.model$glmnet$pred
all.metrics(val.pred$obs, val.pred$pred)
regress.model.glm = regress.model$glmnet
pred.regress.test = predict(regress.model.glm, newdata = expres.test)
time.point.obs = pheno.test$time.point
all.metrics(time.point.obs, pred.regress.test)
[1] "Root Mean Squared Error (RMSE)"
[1] 47.86508
[1] "Mean Absolute Error"
[1] 39.73711
[1] "Median Absolute Error"
[1] 37.99128
[1] "Pearson Correlation Coefficient"
[1] 0.4826416
[1] "Pearson Correlation Signifcance Test"
Pearson's product-moment correlation
data: truth and pred
t = 7.9477, df = 208, p-value = 1.187e-13
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.3715288 0.5801238
sample estimates:
cor
0.4826416
[1] "Spearman Correlation Signifcance Test"
Warning message in cor.test.default(truth, pred, method = "spearman"): “Cannot compute exact p-value with ties”
Spearman's rank correlation rho
data: truth and pred
S = 734230, p-value = 3.161e-16
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.5242953
[1] "R squared"
[1] 0.2329429
[1] "Root Mean Squared Error (RMSE)"
[1] 45.5731
[1] "Mean Absolute Error"
[1] 38.65272
[1] "Median Absolute Error"
[1] 35.31099
[1] "Pearson Correlation Coefficient"
[1] 0.538675
[1] "Pearson Correlation Signifcance Test"
Pearson's product-moment correlation
data: truth and pred
t = 5.7897, df = 82, p-value = 1.253e-07
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.3666206 0.6751030
sample estimates:
cor
0.538675
[1] "Spearman Correlation Signifcance Test"
Warning message in cor.test.default(truth, pred, method = "spearman"): “Cannot compute exact p-value with ties”
Spearman's rank correlation rho
data: truth and pred
S = 39168, p-value = 1.237e-09
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.6034374
[1] "R squared"
[1] 0.2901708
Perform regression only on samples at 90 day time point or less¶
In [100]:
expres.train.90 = expres.train[pheno.train$time.point <= 90,]
pheno.train.90 = pheno.train[pheno.train$time.point <= 90,]
In [106]:
set.seed(100)
pheno.train.90 = dplyr::filter(pheno.train, time.point<=90)
folds.90 = groupKFold(pheno.train.90$monkeyid, k=10)
for (fold in lapply(folds.90, function(x) {pheno.train.90$monkeyid[x]}))
print(length((as.character(fold))))
lapply(folds.90, function(x, y) table(y[x]), y = pheno.train.90$monkeyid)
[1] 129 [1] 124 [1] 119 [1] 110 [1] 119 [1] 114 [1] 119 [1] 114 [1] 124
$Fold1 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 5 5 5 0 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 M38 M4 M5 M6 M7 M8 M9 5 5 5 5 5 5 5 $Fold2 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 5 5 5 5 5 5 5 5 5 0 5 5 5 5 5 5 5 4 5 5 M38 M4 M5 M6 M7 M8 M9 5 5 5 5 5 0 5 $Fold3 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 5 0 5 5 5 5 5 5 5 5 5 5 0 5 5 5 0 4 5 5 M38 M4 M5 M6 M7 M8 M9 5 5 5 5 5 5 5 $Fold4 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 0 5 5 5 5 5 5 0 5 5 5 5 5 0 5 5 5 0 0 5 M38 M4 M5 M6 M7 M8 M9 5 5 5 5 5 5 5 $Fold5 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 5 5 5 5 0 0 5 5 5 5 5 5 5 5 5 5 5 4 5 5 M38 M4 M5 M6 M7 M8 M9 5 5 5 5 0 5 5 $Fold6 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 5 5 0 5 5 5 5 5 0 5 5 5 5 5 5 5 5 4 5 5 M38 M4 M5 M6 M7 M8 M9 0 5 0 5 5 5 5 $Fold7 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 5 5 5 5 5 5 5 5 5 5 0 5 5 5 5 0 5 4 5 0 M38 M4 M5 M6 M7 M8 M9 5 5 5 5 5 5 5 $Fold8 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 5 5 5 5 5 5 5 5 5 5 5 5 5 5 0 5 5 4 5 5 M38 M4 M5 M6 M7 M8 M9 5 0 5 0 5 5 0 $Fold9 M10 M11 M12 M14 M17 M19 M2 M20 M21 M22 M24 M25 M26 M28 M29 M3 M30 M31 M33 M37 5 5 5 5 5 5 0 5 5 5 5 0 5 5 5 5 5 4 5 5 M38 M4 M5 M6 M7 M8 M9 5 5 5 5 5 5 5
In [101]:
set.seed(100)
n = 1000
lambda.grid = c(10 ^ runif(n, min = log10(1e-12), max = log10(1e4)))
alpha.grid = runif(length(lambda.grid), min = 0.00, 1.00)
train.grid = data.frame(lambda = sample(lambda.grid, length(lambda.grid)),
alpha = sample(alpha.grid, length(lambda.grid)))
In [107]:
seed=7
library(doParallel)
start_time <- Sys.time()
cluster = makeCluster(detectCores()-3) # Leaving 3 for other jobs
registerDoParallel(cluster)
methods = c("glmnet")
models = list()
control <- trainControl(method="cv", index=folds.90, savePredictions = 'final', allowParallel=TRUE)
for (alg in methods) {
set.seed(seed)
print("I have gotten to model:")
print(alg)
model = train(expres.train.90, pheno.train.90$time.point, tuneGrid = train.grid, method=alg, trControl=control) #
models[[alg]] = model
}
stopCluster(cluster)
registerDoSEQ()
end_time <- Sys.time()
print(end_time - start_time)
[1] "I have gotten to model:" [1] "glmnet"
Warning message in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : “There were missing values in resampled performance measures.”
Time difference of 7.724496 mins
In [108]:
glmres = models$glmnet$results
graph.hyper(glmres$alpha, log10(glmres$lambda), glmres$MAE)
In [111]:
regress.model.90 = models
val.pred.90 = regress.model.90$glmnet$pred
all.metrics(val.pred.90$obs, val.pred.90$pred)
regress.model.90.glm = regress.model.90$glmnet
pred.regress.test.90 = predict(regress.model.90.glm, newdata = expres.test[pheno.test$time.point <= 90,])
time.point.90 = dplyr::filter(pheno.test, time.point <= 90)$time.point
all.metrics(time.point.90, pred.regress.test.90)
[1] "Root Mean Squared Error (RMSE)"
[1] 20.93642
[1] "Mean Absolute Error"
[1] 16.82706
[1] "Median Absolute Error"
[1] 15.81807
[1] "Pearson Correlation Coefficient"
[1] 0.5149904
[1] "Pearson Correlation Signifcance Test"
Pearson's product-moment correlation
data: truth and pred
t = 6.9025, df = 132, p-value = 1.937e-10
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.3784548 0.6295924
sample estimates:
cor
0.5149904
[1] "Spearman Correlation Signifcance Test"
Warning message in cor.test.default(truth, pred, method = "spearman"): “Cannot compute exact p-value with ties”
Spearman's rank correlation rho
data: truth and pred
S = 188940, p-value = 5.1e-11
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.5288156
[1] "R squared"
[1] 0.2652151
[1] "Root Mean Squared Error (RMSE)"
[1] 22.10746
[1] "Mean Absolute Error"
[1] 16.9703
[1] "Median Absolute Error"
[1] 14.23728
[1] "Pearson Correlation Coefficient"
[1] 0.4550192
[1] "Pearson Correlation Signifcance Test"
Pearson's product-moment correlation
data: truth and pred
t = 3.72, df = 53, p-value = 0.0004825
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
0.2157686 0.6427291
sample estimates:
cor
0.4550192
[1] "Spearman Correlation Signifcance Test"
Warning message in cor.test.default(truth, pred, method = "spearman"): “Cannot compute exact p-value with ties”
Spearman's rank correlation rho
data: truth and pred
S = 14542, p-value = 0.0002446
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.4754011
[1] "R squared"
[1] 0.2070425
Plot Figures 4C-F¶
In [112]:
test.model = data.frame(obs=pheno.test$time.point, pred=pred.regress.test)
test.model.90 = data.frame(obs=time.point.90, pred=pred.regress.test.90)
In [113]:
glm.graph.val = generate.regres.graph(val.pred, "Regression on Time Post Infection\n(Cross-Validation)")
glm.graph.test = generate.regres.graph(test.model, "Regression on Time Post Infection\n(Test)")
glm.graph.val.90 = generate.regres.graph(val.pred.90, "Regression on Time Post Infection\n(Cross-Validation, first 90 days)", break.90=T)
glm.graph.test.90 = generate.regres.graph(test.model.90, "Regression on Time Post Infection\n(Test, first 90 days)", break.90=T)
allregressions= plot_grid(glm.graph.val,
glm.graph.test,
glm.graph.val.90,
glm.graph.test.90,
labels = toupper(c(letters[1:4], align="h")))
allregressions
Figure 3 Human Analysis (Grand Challenges 6-74 cohort)¶
- This code reproduces the analysis of Figure 3, showing that "Blood genome-wide gene expression predicts time since active TB exposure in humans"
Load and Install Required Libraries¶
In [118]:
if (!require("preprocessCore")) {
source("https://bioconductor.org/biocLite.R")
biocLite("preprocessCore")
library("preprocessCore")
}
source("https://bioconductor.org/biocLite.R")
if (!require("Biobase")) {
biocLite("Biobase")
library("Biobase")
}
if (!require("GEOquery")) {
biocLite("GEOquery")
library("GEOquery")
}
if (!require("ggplot2")) {
install.packages("ggplot2")
library("ggplot2")
}
if (!require("glmnet")) {
install.packages("glmnet")
library("glmnet")
}
if (!require("caret")) {
install.packages("caret")
library("caret")
}
if (!require("dplyr")) {
install.packages("dplyr")
library("dplyr")
}
if (!require("ggsignif")) {
install.packages("ggsignif")
library("ggsignif")
}
if (!require("doParallel")) {
install.packages("doParallel")
library("doParallel")
}
if (!require("cowplot")) {
install.packages("cowplot")
library("cowplot")
}
if (!require("e1071")) {
install.packages("e1071")
library("e1071")
}
if (!require("pROC")) {
# pROC 1.12.0 is required, and may not be the default installation:
packageUrl<- "https://cran.r-project.org/src/contrib/Archive/pROC/pROC_1.12.0.tar.gz"
install.packages(packageUrl, repos=NULL, type='source')
library("pROC")
}
Loading required package: preprocessCore Bioconductor version 3.6 (BiocInstaller 1.28.0), ?biocLite for help A new version of Bioconductor is available after installing the most recent version of R; see http://bioconductor.org/install
Load and preprocess data and prepare for machine learning¶
In [37]:
source("utils_submission.R")
Sul.path = paste(path, "/data/GSE94438", sep="")
human.pheno.set = getGEO(filename=paste(Sul.path, "GSE94438_series_matrix.txt.gz", sep="/"),
destdir=Sul.path)
human.pheno = filter.human.pheno(pData(human.pheno.set))
human.exprs.set = read.csv(file=paste(Sul.path, "GSE94438_rawCounts_GeneNames_AllSamples.csv", sep="/"), header=T, row.names = 1)
human.exprs = filter.human.exprs(human.exprs.set, human.pheno)
Parsed with column specification: cols( .default = col_character() ) See spec(...) for full column specifications. Using locally cached version of GPL11154 found here: ./data/GSE94438/GPL11154.soft Warning message in filter.human.pheno(pData(human.pheno.set)): “NAs introduced by coercion”Warning message in filter.human.pheno(pData(human.pheno.set)): “NAs introduced by coercion”Warning message in filter.human.pheno(pData(human.pheno.set)): “NAs introduced by coercion”
age code gender group site subjectid time.from.exposure.months
GSM2475704 NA 672 NA NA NA NA NA
GSM2475705 NA 694 NA NA NA NA NA
GSM2475706 NA 695 NA NA NA NA NA
GSM2475722 NA 994 NA NA NA NA NA
GSM2475742 NA 1061 NA NA NA NA NA
GSM2475748 NA 1194 NA NA NA NA NA
time.to.tb.months
GSM2475704 NA
GSM2475705 NA
GSM2475706 NA
GSM2475722 NA
GSM2475742 NA
GSM2475748 NA
[1] "GSM2475598" "GSM2475603" "GSM2475587" "GSM2475592" "GSM2475599"
[6] "GSM2475604" "GSM2475360" "GSM2475387" "GSM2475362" "GSM2475389"
[11] "GSM2475402" "GSM2475555" "GSM2475361" "GSM2475388" "GSM2475359"
[16] "GSM2475390" "GSM2475588" "GSM2475593" "GSM2475591" "GSM2475596"
[21] "GSM2475589" "GSM2475594" "GSM2475590" "GSM2475595" "GSM2475597"
[26] "GSM2475602" "GSM2475579" "GSM2475601" "GSM2475600" "GSM2475605"
[31] "GSM2475403" "GSM2475554"
[1] 128 128 195 195 217 217 284 284 320 320 45 45 562 562 632 632 740 740 744
[20] 744 746 746 806 806 844 844 897 897 904 904 96 96
418 Levels: 1002 1007 1009 1010 1011 1014 1019 1020 1027 1028 1029 1030 ... 1194
[1] "15"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "72"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "92"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "99"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "358"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "376"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "524"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "729"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "730"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1066"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1114"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1116"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1231"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1233"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "5319"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "5358"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "5360"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "5365"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "08/G329"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "08/G568"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "08/G595"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "09/G131"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "09/G168"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "09/G238"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "09/G403"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "ARHHC16"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "DZHHC38"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "DZHHC84"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KAZHHC50"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KFHHC15"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KHHC04"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KHHC32"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KHHC36"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KHHC41"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "LDHHC18"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "MESHHC04"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "TEKHHC10"
[1] Test
84 Levels: 07/G123 07/G225 07/G437 07/G438 07/G468 08/G245 08/G249 ... W23HHC132
[1] "TEKHHC74"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "W23HHC15"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "W23HHC21"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "W23HHC29"
[1] Test
84 Levels: 07/G123 07/G225 07/G437 07/G438 07/G468 08/G245 08/G249 ... W23HHC132
[1] "W23HHC61"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1115"
[1] Test
21 Levels: 10 11 14 15 18 19 2008 2010 21 23 24 5 6 7 8 9 characterists ... Training
[1] "10/G289"
[1] Test
Levels: 19 21 22 24 failed for QC Test Training
[1] "DZHHC96"
[1] Test
21 Levels: 10 11 14 15 18 19 2008 2010 21 23 24 5 6 7 8 9 characterists ... Training
[1] "Subjects not assigned a training-test set that are now training"
09/G455 09/G476 LDHHC10 91420103 91451104 92245
2 2 1 1 1 1
[1] "about to return new data frame"
[1] "Are all the codes in the expression data in the same order as the codes in pheno?"
[1] TRUE
In [38]:
# Filter out genes whose counts are <= 5 in 50% of samples
exprs.j.keep = apply(human.exprs <= 5, 1, mean) <= 0.5
human.exprs.fil = human.exprs[exprs.j.keep,]
human.exprs.qn = as.data.frame(normalize.quantiles(as.matrix(human.exprs.fil)))
colnames(human.exprs.qn) = colnames(human.exprs.fil)
rownames(human.exprs.qn) = rownames(human.exprs.fil)
human.exprs.log = log2(human.exprs.qn + 1)
human.pheno.6mo = human.pheno
# Randomly sample 50% of AHRI site to training set and test set for this new human.pheno
AHRI.subj = unique(dplyr::filter(human.pheno, site == "AHRI")$subjectid)
set.seed(100)
AHRI.subj.train = sample(AHRI.subj, length(AHRI.subj) / 2)
human.pheno.6mo$dataset[human.pheno.6mo$subjectid %in% AHRI.subj.train] = "Training"
# Filter pheno and splice expression table to include only AHRI and MRC sites and to include only 0 and 6 month time points
human.pheno.6mo = droplevels(dplyr::filter(human.pheno.6mo, site %in% c("AHRI", "MRC"), time.from.exposure.months %in% c(0, 6)))
human.pheno.6mo$status = ifelse(human.pheno.6mo$group == "Control", "control", "progressor")
human.exprs.log.6mo = human.exprs.log[, colnames(human.exprs.log) %in% human.pheno.6mo$code]
dim(human.pheno.6mo)
dim(human.exprs.log.6mo)
head(human.pheno.6mo)
head(human.exprs.log.6mo)
human.pheno.6mo.train = droplevels(dplyr::filter(human.pheno.6mo, dataset == "Training"))
human.pheno.6mo.test = droplevels(dplyr::filter(human.pheno.6mo, dataset != "Training"))
human.exprs.train = t(human.exprs.log.6mo[,as.character(human.pheno.6mo.train$code)])
human.exprs.test = t(human.exprs.log.6mo[, as.character(human.pheno.6mo.test$code)])
set.seed(100)
folds.6mo = groupKFold(human.pheno.6mo.train$subjectid, k=10)
for (fold in lapply(folds.6mo, function(x) {human.pheno.6mo.train$subjectid[x]}))
print(length((as.character(fold))))
lapply(folds.6mo, function(x, y) table(y[x]), y = human.pheno.6mo.train$subjectid)
- 183
- 10
- 14842
- 183
| age | code | gender | group | site | subjectid | time.from.exposure.months | time.to.tb.months | dataset | status |
|---|---|---|---|---|---|---|---|---|---|
| 21 | 231 | F | case (TB) | AHRI | KHHC151 | 6 | 14 | Training | progressor |
| 20 | 235 | F | case (TB) | AHRI | ARHHC63 | 6 | 15 | Test | progressor |
| 26 | 248 | M | case (TB) | MRC | 07/G300 | 0 | 10 | Training | progressor |
| 37 | 267 | M | Control | MRC | 07/G354 | 0 | NA | Training | control |
| 52 | 270 | M | Control | MRC | 07/G419 | 0 | NA | Training | control |
| 16 | 272 | M | case (TB) | MRC | 08/G245 | 6 | 21 | Training | progressor |
| 231 | 235 | 248 | 267 | 270 | 272 | 277 | 280 | 285 | 287 | ⋯ | 1039 | 1040 | 1042 | 1061 | 1062 | 1084 | 1103 | 1164 | 1196 | 1198 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ENSG00000000003 | 3.826839 | 5.279328 | 2.217418 | 3.662508 | 3.885174 | 2.970809 | 4.176669 | 3.862171 | 5.404434 | 5.123012 | ⋯ | 3.180125 | 3.511572 | 3.050806 | 4.116613 | 4.390661 | 3.430704 | 3.462273 | 3.820456 | 3.981523 | 4.013839 |
| ENSG00000000419 | 10.121752 | 10.581316 | 9.294640 | 9.041296 | 9.401630 | 8.575936 | 9.333196 | 8.252973 | 8.657032 | 9.411003 | ⋯ | 9.264556 | 8.536755 | 8.565981 | 9.309794 | 9.017231 | 9.191936 | 8.375149 | 9.565224 | 8.902844 | 8.801773 |
| ENSG00000000457 | 9.459834 | 8.883254 | 9.519146 | 9.757906 | 9.430516 | 9.547550 | 9.566795 | 9.662788 | 9.785935 | 9.889297 | ⋯ | 9.833115 | 9.669295 | 9.875289 | 9.878426 | 9.429308 | 9.901105 | 9.736088 | 9.484452 | 9.909653 | 9.569499 |
| ENSG00000000460 | 6.416530 | 6.349474 | 6.880727 | 6.929954 | 6.691453 | 7.306078 | 6.617279 | 6.199483 | 6.889960 | 6.757782 | ⋯ | 6.474170 | 6.603600 | 5.668603 | 5.783255 | 6.526940 | 6.760125 | 6.606920 | 6.516270 | 6.182759 | 7.985547 |
| ENSG00000000938 | 15.108717 | 14.928224 | 15.240641 | 14.853718 | 14.983593 | 15.225416 | 14.213299 | 15.136584 | 15.240641 | 14.811619 | ⋯ | 14.876220 | 15.194902 | 15.136584 | 14.916084 | 15.413775 | 15.588929 | 15.288608 | 15.280180 | 15.304140 | 14.842417 |
| ENSG00000000971 | 6.363433 | 3.774454 | 5.468685 | 8.320455 | 7.171517 | 4.327480 | 6.725656 | 4.918202 | 6.291429 | 5.876739 | ⋯ | 4.470137 | 4.897124 | 6.403551 | 3.435532 | 6.407795 | 6.964143 | 6.228401 | 5.672357 | 5.804559 | 3.141842 |
[1] 112 [1] 107 [1] 98 [1] 99 [1] 101 [1] 103 [1] 102 [1] 103 [1] 105 [1] 105
$Fold01
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 0 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 1 2 0
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 0 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 1
$Fold02
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 0 1 1 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 0 1 1 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 0 1 1 0
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 0 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
0 1 1 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 1
$Fold03
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 0 1 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 0 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
0 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 1 0 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
0 1 1 2 0 0 2 0
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 0 1 0 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 0 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 1 0
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 0 1 1
$Fold04
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
0 1 1 1 1 1 1 0
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 1 0 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 0 0 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
0 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
0 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 0 0 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 0 1 1 2 2 2 0
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
0 1 1 1 0 1
$Fold05
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 0 0 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
0 1 1 1 1 1 0 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 0 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 0 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
0 1 0 0 2 2 0 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
0 1 2 2 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 0
$Fold06
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 0 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
0 1 1 1 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 0 1 0 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 0 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 0 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 0 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
0 1 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 0 2 2 1 0 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 1
$Fold07
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 0 1 1 2 2 0
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 0 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 0 1 2 0 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 0 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 0 1 0
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 0 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 0 0 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 1
$Fold08
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 1 2 0 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 0 1 0
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 0 1 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 0 1 1 1 0 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 0 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 0 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 0 1 1 1 1
$Fold09
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 1 1 0 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 0 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 0 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 0 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 0 2 0 1 1 1 0
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 0 1 0 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 1
$Fold10
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 1 1 1 0
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 0 1 1 2 1 0 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 1 0 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 0 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 0 0 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 0 1 1 1
Filter genes that differ between 0 and 6 month time points with Wilcoxon test¶
In [39]:
start_time <- Sys.time()
cluster = makeCluster(detectCores()-3) # Leaving 3 for other jobs
registerDoParallel(cluster)
print("Made the clusters")
myFunc <- caretSBF
myFunc$summary <- twoClassSummary
myFunc$score <- function(x, y) {
out <- wilcox.test(x ~ y)$p.value
out
}
filtercontrol.lm = sbfControl(functions = myFunc, method = "cv", index=folds.6mo, allowParallel=TRUE)
set.seed(10)
wilcoxWithFilter.lm = sbf(human.exprs.train, as.factor(human.pheno.6mo.train$time.from.exposure.months), sbfControl = filtercontrol.lm)
print("Did the feature selection")
stopCluster(cluster)
registerDoSEQ()
end_time <- Sys.time()
print(end_time - start_time)
[1] "Made the clusters" [1] "Did the feature selection" Time difference of 11.11606 mins
In [40]:
optvars = wilcoxWithFilter.lm$optVariables
In [41]:
length(optvars)
2524
Perform hyperparameter search on training set to discriminate 0 and 6 month time points post exposure¶
In [42]:
set.seed(100)
n = 1000
lambda.grid = c(10 ^ runif(n, min = log10(1e-6), max = log10(1e2)))
alpha.grid = runif(length(lambda.grid), min = 0.00, 1.00)
train.grid = data.frame(lambda = sample(lambda.grid, length(lambda.grid)),
alpha = sample(alpha.grid, length(lambda.grid)))
In [43]:
time.period.from.exposure = as.factor(ifelse(human.pheno.6mo.train$time.from.exposure.months == "0", "early", "late"))
test.time.period.from.exposure = as.factor(ifelse(human.pheno.6mo.test$time.from.exposure.months == "0", "early", "late"))
In [44]:
seed=7
start_time <- Sys.time()
cluster = makeCluster(detectCores()-3) # Leaving 3 for other jobs
registerDoParallel(cluster)
methods = c("glmnet")
models = list()
control <- trainControl(method="cv", index=folds.6mo, savePredictions = 'final', allowParallel=TRUE,
classProbs=TRUE, summaryFunction=twoClassSummary) # Use AUC to pick the best model
for (alg in methods) {
set.seed(seed)
print("I have gotten to model:")
print(alg)
model = train(human.exprs.train[, colnames(human.exprs.train) %in% c(optvars)],
time.period.from.exposure,
method=alg, tuneGrid = train.grid, trControl=control,
metric="ROC")
models[[alg]] = model
}
stopCluster(cluster)
registerDoSEQ()
end_time <- Sys.time()
print(end_time - start_time)
[1] "I have gotten to model:" [1] "glmnet"
Warning message in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : “There were missing values in resampled performance measures.”
Time difference of 1.90817 mins
In [45]:
glmres = models$glmnet$results
graph.hyper(glmres$alpha, log10(glmres$lambda), glmres$ROC)
Figure 3B¶
In [46]:
human.exposure.model = models$glmnet
glmnet.human.exposure.val.ROC = my.roc(human.exposure.model$pred$early, human.exposure.model$pred$obs, "early")
pred.human.exposure.test.prob = predict(human.exposure.model, newdata = human.exprs.test[, colnames(human.exprs.test) %in% c(optvars)], type="prob")
glmnet.human.exposure.test.ROC <- my.roc(pred.human.exposure.test.prob$early,
test.time.period.from.exposure,
"early", title="Glmnet ROC")
[1] "This is the AUC:" Area under the curve: 0.8986 [1] "This is the AUC p-value:" [1] 1.822595e-13 [1] "This is the AUC 95% Confidence Interval" 95% CI: 0.8418-0.9554 (DeLong) [1] "This is the AUC:" Area under the curve: 0.6879 [1] "This is the AUC p-value:" [1] 0.003761691 [1] "This is the AUC 95% Confidence Interval" 95% CI: 0.561-0.8148 (DeLong)
In [47]:
human.exposure.plot = ggroc(list(CV=glmnet.human.exposure.val.ROC,
test=glmnet.human.exposure.test.ROC),
legacy.axes=TRUE) +
geom_abline(intercept = 0, slope = 1, color = "lightgrey", size = 0.25) +
ggtitle("Baseline vs. 6 Month Time Points") + theme(plot.title = element_text(size=12, face="plain")) +
scale_color_manual(name="Healthy Household Contacts",
labels=c("CV" =expression("CV: AUC 0.90, p ="~1.9~ "x" ~10^{-13} ~ " "),
"test"=expression("Test: AUC 0.69, p = 0.0039")),
values=c("CV"="blue", "test"="red")) +
theme(legend.position=c(0.30,0.20), legend.title = element_text(size=12), legend.text = element_text(size=11))
human.exposure.plot
Figure 3C¶
In [140]:
pred.human.exposure.val = human.exposure.model$pred
pred.human.exposure.test = predict(human.exposure.model, newdata = human.exprs.test[, colnames(human.exprs.test) %in% c(optvars)])
# Calculate ratio of case (TB) to control in all actual and predicted 0 and 6 time periods, in both validation and test set
a.l.0.val = sum(dplyr::filter(human.pheno.6mo.train, time.from.exposure.months=="0")$group == "case (TB)") / length(dplyr::filter(human.pheno.6mo.train, time.from.exposure.months=="0")$group)
a.l.6.val = sum(dplyr::filter(human.pheno.6mo.train, time.from.exposure.months=="6")$group == "case (TB)") / length(dplyr::filter(human.pheno.6mo.train, time.from.exposure.months=="6")$group)
a.l.p.0.val = sum(human.pheno.6mo.train[pred.human.exposure.val$pred == "early",]$group=="case (TB)") / length(human.pheno.6mo.train[pred.human.exposure.val$pred == "early",]$group)
a.l.p.6.val = sum(human.pheno.6mo.train[pred.human.exposure.val$pred == "late",]$group=="case (TB)") / length(human.pheno.6mo.train[pred.human.exposure.val$pred == "late",]$group)
# Calculate ratio of case (TB) to control in all actual and predicted 0 and 6 time periods, in both validation and test set
a.l.0.test = sum(dplyr::filter(human.pheno.6mo.test, time.from.exposure.months=="0")$group == "case (TB)") / length(dplyr::filter(human.pheno.6mo.test, time.from.exposure.months=="0")$group)
a.l.6.test = sum(dplyr::filter(human.pheno.6mo.test, time.from.exposure.months=="6")$group == "case (TB)") / length(dplyr::filter(human.pheno.6mo.test, time.from.exposure.months=="6")$group)
a.l.p.0.test = sum(human.pheno.6mo.test[pred.human.exposure.test == "early",]$group=="case (TB)") / length(human.pheno.6mo.test[pred.human.exposure.test == "early",]$group)
a.l.p.6.test = sum(human.pheno.6mo.test[pred.human.exposure.test == "late",]$group=="case (TB)") / length(human.pheno.6mo.test[pred.human.exposure.test == "late",]$group)
t.period = c("Baseline", "Predicted Baseline", "Baseline", "Predicted Baseline")
partition = as.factor(c("Cross-Validation", "Cross-Validation", "Test", "Test"))
progressor_prop = c(a.l.0.val, a.l.p.0.val, a.l.0.test, a.l.p.0.test)
partition = factor(partition, levels(partition)[c(which(levels(partition) == "Cross-Validation"),
which(levels(partition) == "Test"))])
disease.confound.human = data.frame(t.period=as.factor(t.period), partition=partition, progressor_prop=progressor_prop)
n.act.early.val = sum(dplyr::filter(human.pheno.6mo.train, time.from.exposure.months=="0")$group == "case (TB)")
n.tot.early.val = length(dplyr::filter(human.pheno.6mo.train, time.from.exposure.months=="0")$group)
n.act.p.early.val = sum(human.pheno.6mo.train[pred.human.exposure.val$pred == "early",]$group=="case (TB)")
n.tot.p.early.val = length(human.pheno.6mo.train[pred.human.exposure.val$pred == "early",]$group)
fisher.test(matrix(c(n.act.early.val,
n.tot.early.val - n.act.early.val,
n.act.p.early.val,
n.tot.p.early.val - n.act.p.early.val), ncol=2))
n.act.early.test = sum(dplyr::filter(human.pheno.6mo.test, time.from.exposure.months=="0")$group == "case (TB)")
n.tot.early.test = length(dplyr::filter(human.pheno.6mo.test, time.from.exposure.months=="0")$group)
n.act.p.early.test = sum(human.pheno.6mo.test[pred.human.exposure.test == "early",]$group=="case (TB)")
n.tot.p.early.test = length(human.pheno.6mo.test[pred.human.exposure.test == "early",]$group)
fisher.test(matrix(c(n.act.early.test,
n.tot.early.test - n.act.early.test,
n.act.p.early.test,
n.tot.p.early.test - n.act.p.early.test), ncol=2))
Fisher's Exact Test for Count Data data: p-value = 1 alternative hypothesis: true odds ratio is not equal to 1 95 percent confidence interval: 0.4482757 2.2172025 sample estimates: odds ratio 0.999015
Fisher's Exact Test for Count Data data: p-value = 1 alternative hypothesis: true odds ratio is not equal to 1 95 percent confidence interval: 0.3354192 3.6486152 sample estimates: odds ratio 1.105678
In [141]:
a.l.human.plot = ggplot(disease.confound.human, aes(x=t.period, y=progressor_prop)) + geom_bar(stat="identity") + facet_grid(~partition) +
labs(
y="Proportion of Samples from Humans\nwho progress to Active TB") +
ggtitle("Risk of TB", subtitle="Stratified by Predicted Time Point") + theme(plot.title = element_text(size=12, face="plain", hjust=0.5),
plot.subtitle=element_text(size=11, face="plain", hjust=0.5)) +
ylim(0, 1.0) +
geom_signif(comparisons=list(c("Baseline", "Predicted Baseline")),
y_position=c(0.75),
annotations = c("1.0"), vjust=-0.5,
tip_length=0) + theme(panel.background = element_rect(fill = "white", colour = "white", size = 4)) +
scale_x_discrete(labels=c("Baseline" = "Baseline", "Predicted Baseline"="Predicted\nBaseline")) +
theme(axis.title.x=element_blank())
a.l.human.plot
Reproduce figure 3A, showing that published signatures of risk of TB do not predict time since exposure¶
Load the data (must load previous human data beforehand) and train the models¶
In [142]:
Sul.genes = c("GAS6", "SEPT4", "CD1C", "BLK")
Sul.genes.sel = c("C1QC", "TRAV27", "ANRKD22", "OSBPL10")
ACS.genes = c("ANKRD22",
"APOL1",
"BATF2",
"ETV7",
"FCGR1A",
"FCGR1B",
"GBP1",
"GBP2",
"GBP4",
"GBP5",
"SCARF1",
"SEPT4",
"SERPING1",
"STAT1",
"TAP1",
"TRAFD1"
)
ACS.transcripts = row.names(human.exprs.set)[match(unique(ACS.genes), human.exprs.set$symbol)]
Sul.transcripts = row.names(human.exprs.set)[match(unique(Sul.genes), human.exprs.set$symbol)]
Sul.transcripts.sel = row.names(human.exprs.set)[match(unique(Sul.genes.sel), human.exprs.set$symbol)]
human.exprs.train.ACS = human.exprs.train[, colnames(human.exprs.train) %in% ACS.transcripts]
human.exprs.train.Sul = human.exprs.train[, colnames(human.exprs.train) %in% Sul.transcripts]
human.exprs.train.Sul.sel = human.exprs.train[, colnames(human.exprs.train) %in% Sul.transcripts.sel]
human.exprs.test.ACS = human.exprs.test[, colnames(human.exprs.test) %in% ACS.transcripts]
human.exprs.test.Sul = human.exprs.test[, colnames(human.exprs.test) %in% Sul.transcripts]
human.exprs.test.Sul.sel = human.exprs.test[, colnames(human.exprs.test) %in% Sul.transcripts.sel]
human.exprs.train.TB.genes = human.exprs.train[, colnames(human.exprs.train) %in% c(ACS.transcripts,
Sul.transcripts,
Sul.transcripts.sel)]
human.exprs.test.TB.genes = human.exprs.test[, colnames(human.exprs.test) %in% c(ACS.transcripts,
Sul.transcripts,
Sul.transcripts.sel)]
time.period.from.exposure = as.factor(ifelse(human.pheno.6mo.train$time.from.exposure.months == "0", "early", "late"))
test.time.period.from.exposure = as.factor(ifelse(human.pheno.6mo.test$time.from.exposure.months == "0", "early", "late"))
In [145]:
seed=7
start_time <- Sys.time()
cluster = makeCluster(detectCores()-3) # Leaving 3 for other jobs
registerDoParallel(cluster)
methods = c("glmnet")
models = list()
control <- trainControl(method="cv", index=folds.6mo, search="random", savePredictions = 'final', allowParallel=TRUE,
classProbs=TRUE, summaryFunction=twoClassSummary) # Use AUC to pick the best model
for (alg in methods) {
set.seed(seed)
print("I have gotten to model:")
print(alg)
model = train(human.exprs.train.ACS,
as.factor(time.period.from.exposure),
method=alg, tuneLength=30, trControl=control,
metric="ROC")
models[[alg]] = model
}
stopCluster(cluster)
registerDoSEQ()
end_time <- Sys.time()
print(end_time - start_time)
[1] "I have gotten to model:" [1] "glmnet"
Warning message in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : “There were missing values in resampled performance measures.”
Time difference of 40.93105 secs
In [146]:
ACS.human.exposure.model = models$glmnet
In [147]:
seed=7
start_time <- Sys.time()
cluster = makeCluster(detectCores()-3) # Leaving 3 for other jobs
registerDoParallel(cluster)
methods = c("glmnet")
models = list()
control <- trainControl(method="cv", index=folds.6mo, search="random", savePredictions = 'final', allowParallel=TRUE,
classProbs=TRUE, summaryFunction=twoClassSummary) # Use AUC to pick the best model
for (alg in methods) {
set.seed(seed)
print("I have gotten to model:")
print(alg)
model = train(human.exprs.train.Sul,
as.factor(time.period.from.exposure),
method=alg, tuneLength=30, trControl=control,
metric="ROC")
models[[alg]] = model
}
stopCluster(cluster)
registerDoSEQ()
end_time <- Sys.time()
print(end_time - start_time)
[1] "I have gotten to model:" [1] "glmnet"
Warning message in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : “There were missing values in resampled performance measures.”
Time difference of 32.12168 secs
In [148]:
RISK4.human.exposure.model =models$glmnet
In [149]:
seed=7
start_time <- Sys.time()
cluster = makeCluster(detectCores()-3) # Leaving 3 for other jobs
registerDoParallel(cluster)
methods = c("glmnet")
models = list()
control <- trainControl(method="cv", index=folds.6mo, search="random", savePredictions = 'final', allowParallel=TRUE,
classProbs=TRUE, summaryFunction=twoClassSummary) # Use AUC to pick the best model
for (alg in methods) {
set.seed(seed)
print("I have gotten to model:")
print(alg)
model = train(human.exprs.train.Sul.sel,
as.factor(time.period.from.exposure),
method=alg, tuneLength=30, trControl=control,
metric="ROC")
models[[alg]] = model
}
stopCluster(cluster)
registerDoSEQ()
end_time <- Sys.time()
print(end_time - start_time)
[1] "I have gotten to model:" [1] "glmnet"
Warning message in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : “There were missing values in resampled performance measures.”
Time difference of 32.76669 secs
In [150]:
Sul.sel.human.exposure.model = models$glmnet
In [151]:
seed=7
start_time <- Sys.time()
cluster = makeCluster(detectCores()-3) # Leaving 3 for other jobs
registerDoParallel(cluster)
methods = c("glmnet")
models = list()
control <- trainControl(method="cv", index=folds.6mo, search="random", savePredictions = 'final', allowParallel=TRUE,
classProbs=TRUE, summaryFunction=twoClassSummary) # Use AUC to pick the best model
for (alg in methods) {
set.seed(seed)
print("I have gotten to model:")
print(alg)
model = train(human.exprs.train.TB.genes,
as.factor(time.period.from.exposure),
method=alg, tuneLength=30, trControl=control,
metric="ROC")
models[[alg]] = model
}
stopCluster(cluster)
registerDoSEQ()
end_time <- Sys.time()
print(end_time - start_time)
[1] "I have gotten to model:" [1] "glmnet"
Warning message in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : “There were missing values in resampled performance measures.”
Time difference of 32.29566 secs
In [152]:
TB.genes.human.exposure.model = models$glmnet
Figure 3A¶
In [153]:
pred.human.test.ACS.exposure = predict(ACS.human.exposure.model, newdata = human.exprs.test.ACS, type="prob")
glmnet.human.test.ACS.exposure.ROC <- my.roc(pred.human.test.ACS.exposure$early,
test.time.period.from.exposure,
"early", title="Glmnet ROC")
[1] "This is the AUC:" Area under the curve: 0.5815 [1] "This is the AUC p-value:" [1] 0.1269821 [1] "This is the AUC 95% Confidence Interval" 95% CI: 0.4387-0.7243 (DeLong)
In [154]:
pred.human.test.RISK4.exposure = predict(RISK4.human.exposure.model, newdata = human.exprs.test.Sul , type="prob")
glmnet.human.test.RISK4.exposure.ROC <- my.roc(pred.human.test.RISK4.exposure$early,
test.time.period.from.exposure,
"early", title="Glmnet ROC")
[1] "This is the AUC:" Area under the curve: 0.5527 [1] "This is the AUC p-value:" [1] 0.7724687 [1] "This is the AUC 95% Confidence Interval" 95% CI: 0.4103-0.6952 (DeLong)
In [155]:
pred.human.test.Sul.sel.exposure = predict(Sul.sel.human.exposure.model, newdata = human.exprs.test.Sul.sel , type="prob")
glmnet.human.test.Sul.sel.exposure.ROC <- my.roc(pred.human.test.Sul.sel.exposure$early,
test.time.period.from.exposure,
"early", title="Glmnet ROC")
[1] "This is the AUC:" Area under the curve: 0.5514 [1] "This is the AUC p-value:"
Warning message in wilcox.test.default(pred[obs == 1], pred[obs == 0], alternative = "great"): “cannot compute exact p-value with ties”
[1] 0.2356543 [1] "This is the AUC 95% Confidence Interval" 95% CI: 0.4123-0.6905 (DeLong)
In [156]:
pred.human.test.TB.genes.exposure = predict(TB.genes.human.exposure.model, newdata = human.exprs.test.TB.genes , type="prob")
glmnet.human.test.TB.genes.exposure.ROC <- my.roc(pred.human.test.TB.genes.exposure$early,
test.time.period.from.exposure,
"early", title="Glmnet ROC")
[1] "This is the AUC:" Area under the curve: 0.5623 [1] "This is the AUC p-value:" [1] 0.1921917 [1] "This is the AUC 95% Confidence Interval" 95% CI: 0.4197-0.7049 (DeLong)
In [157]:
human.riskgenes.exposure.plot = ggroc(list(ACS=glmnet.human.test.ACS.exposure.ROC,
RISK4=glmnet.human.test.RISK4.exposure.ROC,
Sel=glmnet.human.test.Sul.sel.exposure.ROC,
TB=glmnet.human.test.TB.genes.exposure.ROC),
legacy.axes=TRUE) +
geom_abline(intercept = 0, slope = 1, color = "lightgrey", size = 0.25) +
ggtitle("Baseline vs. 6 Month Time Points\n(TB Risk Genes)") + theme(plot.title = element_text(size=12, face="plain")) +
scale_color_manual(name=" Healthy Household Contacts",
labels=c("ACS" =expression("ACS COR: AUC 0.58, p = 0.13"),
"RISK4"=expression("RISK4: AUC 0.55, p = 0.77"),
"Sel"=expression("Suliman et al post hoc:\nAUC 0.55, p = 0.24"),
"TB"=expression("All: AUC 0.56, p = 0.19")),
values=c("ACS"="red", "RISK4"="blue", "Sel"="darkgreen", "TB"="black")) +
theme(legend.position=c(0.30,0.20), legend.title = element_text(size=12), legend.text = element_text(size=11))
human.riskgenes.exposure.plot
Reproduce figure 3D, showing that the genes in our signature of time since exposure cannot predict risk of TB in the GC6-74 cohort.¶
Select the genes in our signature and train a model with these genes to predict risk of TB progression.¶
In [158]:
human.exposure.glmnet = human.exposure.model$finalModel
coefs.human.6mo = coef(human.exposure.glmnet , s=models$glmnet$bestTune$lambda)
nonzero.coefs.r = data.frame(name = coefs.human.6mo@Dimnames[[1]][coefs.human.6mo@i + 1], coefficient = coefs.human.6mo@x)
sort.coeffs.r = nonzero.coefs.r[order(-abs(nonzero.coefs.r$coefficient)), ]#, sort(abs(nonzero.coefs$coefficient), T)
genes.coefs.r = sort.coeffs.r$name[2:length(sort.coeffs.r$name)]
In [159]:
length(genes.coefs.r)
254
In [161]:
set.seed(100)
n = 1000
lambda.grid = c(10 ^ runif(n, min = log10(1e-6), max = log10(1e2)))
alpha.grid = runif(length(lambda.grid), min = 0.00, 1.00)
train.grid = data.frame(lambda = sample(lambda.grid, length(lambda.grid)),
alpha = sample(alpha.grid, length(lambda.grid)))
In [162]:
seed=7
start_time <- Sys.time()
cluster = makeCluster(detectCores()-3) # Leaving 3 for other jobs
registerDoParallel(cluster)
methods = c("glmnet")
models = list()
control <- trainControl(method="cv", index=folds.6mo, savePredictions = 'final', allowParallel=TRUE,
classProbs=TRUE, summaryFunction=twoClassSummary) # Use AUC to pick the best model
for (alg in methods) {
set.seed(seed)
print("I have gotten to model:")
print(alg)
model = train(human.exprs.train[,colnames(human.exprs.train) %in% as.character(genes.coefs.r)],
as.factor(human.pheno.6mo.train$status),
method=alg, tuneGrid = train.grid, trControl=control,
metric="ROC")
models[[alg]] = model
}
stopCluster(cluster)
registerDoSEQ()
end_time <- Sys.time()
print(end_time - start_time)
[1] "I have gotten to model:" [1] "glmnet"
Warning message in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : “There were missing values in resampled performance measures.”
Time difference of 57.74353 secs
In [163]:
human.TB.from.exposure.model = models$glmnet
exposure.genes = genes.coefs.r
Figure 3D¶
In [166]:
pred.human.TB.from.exposure.test = predict(human.TB.from.exposure.model, newdata = human.exprs.test[,colnames(human.exprs.test) %in% as.character(exposure.genes)])
confusionMatrix(human.TB.from.exposure.model$pred$pred, human.TB.from.exposure.model$pred$obs, positive= "progressor")
confusionMatrix(pred.human.TB.from.exposure.test, as.factor(human.pheno.6mo.test$status), positive = "progressor")
glmnet.human.TB.from.exposure.val.ROC = my.roc(human.TB.from.exposure.model$pred$progressor, human.TB.from.exposure.model$pred$obs, "progressor")
pred.human.TB.from.exposure.test.prob = predict(human.TB.from.exposure.model, newdata = human.exprs.test[,colnames(human.exprs.test) %in% as.character(exposure.genes)], type="prob")
glmnet.human.TB.from.exposure.test.ROC <- my.roc(pred.human.TB.from.exposure.test.prob$progressor,
as.factor(human.pheno.6mo.test$status),
"progressor", title="Glmnet ROC")
Confusion Matrix and Statistics
Reference
Prediction control progressor
control 83 32
progressor 0 0
Accuracy : 0.7217
95% CI : (0.6305, 0.8013)
No Information Rate : 0.7217
P-Value [Acc > NIR] : 0.5475
Kappa : 0
Mcnemar's Test P-Value : 4.251e-08
Sensitivity : 0.0000
Specificity : 1.0000
Pos Pred Value : NaN
Neg Pred Value : 0.7217
Prevalence : 0.2783
Detection Rate : 0.0000
Detection Prevalence : 0.0000
Balanced Accuracy : 0.5000
'Positive' Class : progressor
Confusion Matrix and Statistics
Reference
Prediction control progressor
control 52 16
progressor 0 0
Accuracy : 0.7647
95% CI : (0.6462, 0.8591)
No Information Rate : 0.7647
P-Value [Acc > NIR] : 0.5665583
Kappa : 0
Mcnemar's Test P-Value : 0.0001768
Sensitivity : 0.0000
Specificity : 1.0000
Pos Pred Value : NaN
Neg Pred Value : 0.7647
Prevalence : 0.2353
Detection Rate : 0.0000
Detection Prevalence : 0.0000
Balanced Accuracy : 0.5000
'Positive' Class : progressor
[1] "This is the AUC:" Area under the curve: 0.5693 [1] "This is the AUC p-value:" [1] 0.8752258 [1] "This is the AUC 95% Confidence Interval" 95% CI: 0.4409-0.6977 (DeLong) [1] "This is the AUC:" Area under the curve: 0.6322 [1] "This is the AUC p-value:" [1] 0.05669511 [1] "This is the AUC 95% Confidence Interval" 95% CI: 0.4645-0.7999 (DeLong)
In [165]:
human.TB.from.exposure.plot = ggroc(list(CV=glmnet.human.TB.from.exposure.val.ROC ,
test=glmnet.human.TB.from.exposure.test.ROC ),
legacy.axes=TRUE) +
geom_abline(intercept = 0, slope = 1, color = "lightgrey", size = 0.25) +
ggtitle("Risk of TB Prediction\n(Time Since Exposure Genes)") + theme(plot.title = element_text(size=12, face="plain")) +
scale_color_manual(name="Healthy Household Contacts",
labels=c("CV" =expression("CV: AUC 0.56, p = 0.85"),
"test"=expression("Test: AUC 0.62, p = 0.069")),
values=c("CV"="blue", "test"="red")) +
theme(legend.position=c(0.30,0.20), legend.title = element_text(size=12), legend.text = element_text(size=11))
human.TB.from.exposure.plot
Figure 4 Human Analysis (Grand Challenges 6-74 cohort, Adolescent Cohort Study, Singhania et al UK, South Africa and Leicester cohort studies, Small Adolescent Infection Acquisition study)¶
- This code reproduces the analysis of Figure 4, showing that "Application of gene expression signature of time since active TB exposure to ACS cohort and Leicester cohort confirms its identification of recent exposure in humans"
Load and Install Required Libraries¶
In [4]:
if (!require("preprocessCore")) {
source("https://bioconductor.org/biocLite.R")
biocLite("preprocessCore")
library("preprocessCore")
}
source("https://bioconductor.org/biocLite.R")
if (!require("Biobase")) {
biocLite("Biobase")
library("Biobase")
}
if (!require("GEOquery")) {
biocLite("GEOquery")
library("GEOquery")
}
if (!require("ggplot2")) {
install.packages("ggplot2")
library("ggplot2")
}
if (!require("glmnet")) {
install.packages("glmnet")
library("glmnet")
}
if (!require("caret")) {
install.packages("caret")
library("caret")
}
if (!require("dplyr")) {
install.packages("dplyr")
library("dplyr")
}
if (!require("ggsignif")) {
install.packages("ggsignif")
library("ggsignif")
}
if (!require("doParallel")) {
install.packages("doParallel")
library("doParallel")
}
if (!require("cowplot")) {
install.packages("cowplot")
library("cowplot")
}
if (!require("e1071")) {
install.packages("e1071")
library("e1071")
}
if (!require("pROC")) {
# pROC 1.12.0 is required, and may not be the default installation:
packageUrl<- "https://cran.r-project.org/src/contrib/Archive/pROC/pROC_1.12.0.tar.gz"
install.packages(packageUrl, repos=NULL, type='source')
library("pROC")
}
Loading required package: preprocessCore
Bioconductor version 3.6 (BiocInstaller 1.28.0), ?biocLite for help
A new version of Bioconductor is available after installing the most recent
version of R; see http://bioconductor.org/install
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
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anyDuplicated, append, as.data.frame, cbind, colMeans, colnames,
colSums, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
grepl, intersect, is.unsorted, lapply, lengths, Map, mapply, match,
mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
rbind, Reduce, rowMeans, rownames, rowSums, sapply, setdiff, sort,
table, tapply, union, unique, unsplit, which, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
Loading required package: GEOquery
Setting options('download.file.method.GEOquery'='auto')
Setting options('GEOquery.inmemory.gpl'=FALSE)
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Attaching package: ‘dplyr’
The following object is masked from ‘package:Biobase’:
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Loading required package: iterators
Loading required package: cowplot
Attaching package: ‘cowplot’
The following object is masked from ‘package:ggplot2’:
ggsave
Loading required package: pROC
Type 'citation("pROC")' for a citation.
Attaching package: ‘pROC’
The following object is masked from ‘package:glmnet’:
auc
The following object is masked from ‘package:BiocGenerics’:
var
The following objects are masked from ‘package:stats’:
cov, smooth, var
Reproduce Analysis for Figure 4A¶
Load and co-process the data from the ARCHS4 resource for GC6-74 and ACS¶
To download data from the ARCHS4 resource¶
- In a web browser go to https://amp.pharm.mssm.edu/archs4/data.html
- Search for GSE94438 (GC6-74) and click the download link (a zipped folder with a gene_count.tsv file is downloaded)
- Do the same for for GSE79362 (ACS)
- Move the respective files into their data/GSE... folder in this repository and rename: "gene_count_GSE[GSE # here].tsv"
In [168]:
source("utils_submission.R")
Sul.path = paste(path, "/data/GSE94438", sep="")
ACS.path = paste(path, "/data/GSE79362", sep="")
Sul.pheno.set = getGEO(filename=paste(Sul.path, "GSE94438_series_matrix.txt.gz", sep="/"),
destdir=Sul.path)
Sul.pheno = filter.human.pheno(pData(Sul.pheno.set))
ACS.pheno.set = getGEO(filename=paste(ACS.path, "GSE79362_series_matrix.txt.gz", sep="/"),
destdir=Sul.path)
ACS.pheno = filter.ACS.pheno(pData(ACS.pheno.set))
Parsed with column specification: cols( .default = col_character() ) See spec(...) for full column specifications. Using locally cached version of GPL11154 found here: /master/rault/TB/data/GSE94438/GPL11154.soft Warning message in filter.human.pheno(pData(human.pheno.set)): “NAs introduced by coercion”Warning message in filter.human.pheno(pData(human.pheno.set)): “NAs introduced by coercion”Warning message in filter.human.pheno(pData(human.pheno.set)): “NAs introduced by coercion”
age code gender group site subjectid time.from.exposure.months
GSM2475704 NA 672 NA NA NA NA NA
GSM2475705 NA 694 NA NA NA NA NA
GSM2475706 NA 695 NA NA NA NA NA
GSM2475722 NA 994 NA NA NA NA NA
GSM2475742 NA 1061 NA NA NA NA NA
GSM2475748 NA 1194 NA NA NA NA NA
time.to.tb.months
GSM2475704 NA
GSM2475705 NA
GSM2475706 NA
GSM2475722 NA
GSM2475742 NA
GSM2475748 NA
[1] "GSM2475598" "GSM2475603" "GSM2475587" "GSM2475592" "GSM2475599"
[6] "GSM2475604" "GSM2475360" "GSM2475387" "GSM2475362" "GSM2475389"
[11] "GSM2475402" "GSM2475555" "GSM2475361" "GSM2475388" "GSM2475359"
[16] "GSM2475390" "GSM2475588" "GSM2475593" "GSM2475591" "GSM2475596"
[21] "GSM2475589" "GSM2475594" "GSM2475590" "GSM2475595" "GSM2475597"
[26] "GSM2475602" "GSM2475579" "GSM2475601" "GSM2475600" "GSM2475605"
[31] "GSM2475403" "GSM2475554"
[1] 128 128 195 195 217 217 284 284 320 320 45 45 562 562 632 632 740 740 744
[20] 744 746 746 806 806 844 844 897 897 904 904 96 96
418 Levels: 1002 1007 1009 1010 1011 1014 1019 1020 1027 1028 1029 1030 ... 1194
[1] "15"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "72"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "92"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "99"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "358"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "376"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "524"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "729"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "730"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1066"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1114"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1116"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1231"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1233"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "5319"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "5358"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "5360"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "5365"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "08/G329"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "08/G568"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "08/G595"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "09/G131"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "09/G168"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "09/G238"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "09/G403"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "ARHHC16"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "DZHHC38"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "DZHHC84"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KAZHHC50"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KFHHC15"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KHHC04"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KHHC32"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KHHC36"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KHHC41"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "LDHHC18"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "MESHHC04"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "TEKHHC10"
[1] Test
84 Levels: 07/G123 07/G225 07/G437 07/G438 07/G468 08/G245 08/G249 ... W23HHC132
[1] "TEKHHC74"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "W23HHC15"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "W23HHC21"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "W23HHC29"
[1] Test
84 Levels: 07/G123 07/G225 07/G437 07/G438 07/G468 08/G245 08/G249 ... W23HHC132
[1] "W23HHC61"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1115"
[1] Test
21 Levels: 10 11 14 15 18 19 2008 2010 21 23 24 5 6 7 8 9 characterists ... Training
[1] "10/G289"
[1] Test
Levels: 19 21 22 24 failed for QC Test Training
[1] "DZHHC96"
[1] Test
21 Levels: 10 11 14 15 18 19 2008 2010 21 23 24 5 6 7 8 9 characterists ... Training
[1] "Subjects not assigned a training-test set that are now training"
09/G455 09/G476 LDHHC10 91420103 91451104 92245
2 2 1 1 1 1
[1] "about to return new data frame"
Parsed with column specification: cols( .default = col_character() ) See spec(...) for full column specifications. Using locally cached version of GPL11154 found here: /master/rault/TB/data/GSE94438/GPL11154.soft Warning message in filter.ACS.pheno(pData(ACS.pheno.set)): “NAs introduced by coercion”
[1] "about to return new data frame"
In [171]:
# Common names for progressor
Sul.pheno$group = ifelse(Sul.pheno$group == "Control", "control", "progressor")
ACS.pheno$group = ifelse(ACS.pheno$group == "control (non-progressor)", "control", "progressor")
ACS.exprs = read.table(paste(ACS.path, "gene_count_GSE79362.tsv", sep="/"), header=T, row.names=1)
ACS.exprs = filter.HUMAN.exprs(ACS.exprs, ACS.pheno)
Sul.exprs = read.table(paste(Sul.path, "gene_count_GSE94438.tsv", sep="/"), header=T, row.names=1)
Sul.exprs = filter.HUMAN.exprs(Sul.exprs, Sul.pheno)
all.exprs = cbind(ACS.exprs, Sul.exprs)
# Filter out genes whose counts are <= 5 in 50% of samples
exprs.j.keep = apply(all.exprs <= 5, 1, mean) <= 0.5
exprs.fil = all.exprs[exprs.j.keep,]
exprs.qn = as.data.frame(normalize.quantiles(as.matrix(exprs.fil)))
colnames(exprs.qn) = colnames(exprs.fil)
rownames(exprs.qn) = rownames(exprs.fil)
exprs.log = log2(exprs.qn + 1)
Sul.exprs.log = exprs.log[, colnames(exprs.log) %in% row.names(Sul.pheno)]
ACS.exprs.log = exprs.log[, colnames(exprs.log) %in% row.names(ACS.pheno)]
ACS.exprs.log = t(ACS.exprs.log)
[1] "Identical column and rownames between exprs and pheno tables?" [1] TRUE [1] "Identical column and rownames between exprs and pheno tables?" [1] TRUE
Repeat process of Figure 3B to build a model from GC6-74 data to predict time since TB exposure¶
In [176]:
Sul.pheno.6mo = Sul.pheno
# Randomly sample 50% of AHRI site to training set and test set for this new Sul.pheno
AHRI.subj = unique(dplyr::filter(Sul.pheno, site == "AHRI")$subjectid)
set.seed(100)
AHRI.subj.train = sample(AHRI.subj, length(AHRI.subj) / 2)
Sul.pheno.6mo$dataset[Sul.pheno.6mo$subjectid %in% AHRI.subj.train] = "Training"
# dplyr::filter pheno and expression table to include only AHRI and MRC sites and to include only 0 and 6 month time points
Sul.pheno.6mo = droplevels(Sul.pheno.6mo[Sul.pheno.6mo$site %in% c("AHRI", "MRC") & Sul.pheno.6mo$time.from.exposure.months %in% c(0, 6),])
Sul.exprs.log.6mo = Sul.exprs.log[, colnames(Sul.exprs.log) %in% row.names(Sul.pheno.6mo)]
Sul.pheno.6mo.train = droplevels(Sul.pheno.6mo[ Sul.pheno.6mo$dataset == "Training",])
Sul.pheno.6mo.test = droplevels(Sul.pheno.6mo[ Sul.pheno.6mo$dataset != "Training",])
Sul.exprs.train = t(Sul.exprs.log.6mo[,row.names(Sul.pheno.6mo.train)])
Sul.exprs.test = t(Sul.exprs.log.6mo[, row.names(Sul.pheno.6mo.test)])
set.seed(100)
folds.6mo = groupKFold(Sul.pheno.6mo.train$subjectid, k=10)
for (fold in lapply(folds.6mo, function(x) {Sul.pheno.6mo.train$subjectid[x]}))
print(length((as.character(fold))))
lapply(folds.6mo, function(x, y) table(y[x]), y = Sul.pheno.6mo.train$subjectid)
time.period.from.exposure = as.factor(ifelse(Sul.pheno.6mo.train$time.from.exposure.months == "0", "early", "late"))
test.time.period.from.exposure = as.factor(ifelse(Sul.pheno.6mo.test$time.from.exposure.months == "0", "early", "late"))
[1] 112 [1] 107 [1] 98 [1] 99 [1] 101 [1] 103 [1] 102 [1] 103 [1] 105 [1] 105
$Fold01
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 0 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 1 2 0
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 0 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 1
$Fold02
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 0 1 1 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 0 1 1 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 0 1 1 0
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 0 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
0 1 1 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 1
$Fold03
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 0 1 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 0 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
0 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 1 0 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
0 1 1 2 0 0 2 0
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 0 1 0 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 0 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 1 0
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 0 1 1
$Fold04
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
0 1 1 1 1 1 1 0
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 1 0 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 0 0 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
0 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
0 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 0 0 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 0 1 1 2 2 2 0
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
0 1 1 1 0 1
$Fold05
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 0 0 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
0 1 1 1 1 1 0 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 0 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 0 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
0 1 0 0 2 2 0 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
0 1 2 2 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 0
$Fold06
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 0 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
0 1 1 1 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 0 1 0 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 0 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 0 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 0 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
0 1 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 0 2 2 1 0 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 1
$Fold07
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 0 1 1 2 2 0
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 0 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 0 1 2 0 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 0 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 0 1 0
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 0 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 0 0 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 1
$Fold08
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 1 2 0 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 0 1 0
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 0 1 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 0 1 1 1 0 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 0 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 0 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 0 1 1 1 1
$Fold09
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 1 1 0 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 0 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 0 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 0 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 0 2 0 1 1 1 0
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 0 1 0 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 1
$Fold10
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 1 1 1 0
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 0 1 1 2 1 0 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 1 0 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 0 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 0 0 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 0 1 1 1
In [177]:
start_time <- Sys.time()
cluster = makeCluster(detectCores()-3) # Leaving 3 for other jobs
registerDoParallel(cluster)
print("Made the clusters")
myFunc <- caretSBF
myFunc$summary <- twoClassSummary
myFunc$score <- function(x, y) {
out <- wilcox.test(x ~ y)$p.value # Will have warnings due to ties; ignore for now.
out
}
filtercontrol.lm = sbfControl(functions = myFunc, method = "cv", index=folds.6mo, allowParallel=TRUE)
set.seed(10)
wilcoxWithFilter.lm = sbf(Sul.exprs.train, as.factor(Sul.pheno.6mo.train$time.from.exposure.months), sbfControl = filtercontrol.lm)
print("Did the feature selection")
stopCluster(cluster)
registerDoSEQ()
end_time <- Sys.time()
print(end_time - start_time)
[1] "Made the clusters" [1] "Did the feature selection" Time difference of 12.02605 mins
In [178]:
opt.Sul.vars = wilcoxWithFilter.lm$optVariables
In [179]:
set.seed(100)
n = 1000
lambda.grid = c(10 ^ runif(n, min = log10(1e-6), max = log10(1e2)))
alpha.grid = runif(length(lambda.grid), min = 0.00, 1.00)
train.grid = data.frame(lambda = sample(lambda.grid, length(lambda.grid)),
alpha = sample(alpha.grid, length(lambda.grid)))
In [180]:
seed=7
start_time <- Sys.time()
cluster = makeCluster(detectCores()-3) # Leaving 3 for other jobs
registerDoParallel(cluster)
methods = c("glmnet")
models = list()
control <- trainControl(method="cv", index=folds.6mo, savePredictions = 'final', allowParallel=TRUE,
classProbs=TRUE, summaryFunction=twoClassSummary) # Use AUC to pick the best model
for (alg in methods) {
set.seed(seed)
print("I have gotten to model:")
print(alg)
model = train(Sul.exprs.train[, colnames(Sul.exprs.train) %in% c(opt.Sul.vars)],
time.period.from.exposure,
method=alg, tuneGrid = train.grid, trControl=control,
metric="ROC")
models[[alg]] = model
}
stopCluster(cluster)
registerDoSEQ()
end_time <- Sys.time()
print(end_time - start_time)
[1] "I have gotten to model:" [1] "glmnet"
Warning message in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : “There were missing values in resampled performance measures.”
Time difference of 2.016605 mins
In [181]:
glmres = models$glmnet$results
graph.hyper (x=glmres$alpha, y=log10(glmres$lambda), z=glmres$ROC)
In [182]:
human.expose.Sul.ACS.model = models$glmnet
In [183]:
glmnet.Sul.val.ROC = my.roc(human.expose.Sul.ACS.model$pred$early, human.expose.Sul.ACS.model$pred$obs, "early")
pred.Sul.test.prob = predict(human.expose.Sul.ACS.model, newdata = Sul.exprs.test[, colnames(Sul.exprs.test) %in% c(opt.Sul.vars)], type="prob")
glmnet.Sul.test.ROC <- my.roc(pred.Sul.test.prob$early,
test.time.period.from.exposure,
"early", title="Glmnet ROC")
[1] "This is the AUC:" Area under the curve: 0.8871 [1] "This is the AUC p-value:" [1] 8.429174e-13 [1] "This is the AUC 95% Confidence Interval" 95% CI: 0.8234-0.9508 (DeLong) [1] "This is the AUC:" Area under the curve: 0.6548 [1] "This is the AUC p-value:" [1] 0.01431948 [1] "This is the AUC 95% Confidence Interval" 95% CI: 0.5237-0.7858 (DeLong)
Figure 4A¶
In [184]:
pred.ACS.expose = predict(human.expose.Sul.ACS.model, newdata = ACS.exprs.log[, colnames(ACS.exprs.log) %in% c(opt.Sul.vars)])
# Proportion of progressor samples with an early time point prediction
prop.ACS.prog.early = sum(pred.ACS.expose == "early" & ACS.pheno$group == "progressor") / sum(ACS.pheno$group == "progressor")
prop.ACS.prog.early
# Proportion of control samples with an early time point prediction
prop.ACS.ctrl.early = sum(pred.ACS.expose == "early" & ACS.pheno$group == "control") / sum(ACS.pheno$group == "control")
fisher.test(matrix(c(sum(pred.ACS.expose == "early" & ACS.pheno$group == "progressor"),
sum(ACS.pheno$group == "progressor") - sum(pred.ACS.expose == "early" & ACS.pheno$group == "progressor"),
sum(pred.ACS.expose == "early" & ACS.pheno$group == "control"),
sum(ACS.pheno$group == "control") - sum(pred.ACS.expose == "early" & ACS.pheno$group == "control")),
ncol=2))
dis.status = c("Control", "Progressor")
early_prop = c(prop.ACS.ctrl.early, prop.ACS.prog.early)
ACS.exposure.prediction = data.frame(dis.status=as.factor(dis.status), early_prop=early_prop)
0.954545454545455
Fisher's Exact Test for Count Data data: p-value = 0.7696 alternative hypothesis: true odds ratio is not equal to 1 95 percent confidence interval: 0.2344173 3.1198867 sample estimates: odds ratio 0.8013923
In [185]:
ACS.exposure.plot = ggplot(ACS.exposure.prediction, aes(x=dis.status, y=early_prop)) + geom_bar(stat="identity") +
labs(
y="Proportion of Samples\nPredicted as Baseline Timepoint") +
ggtitle("Predicted Time Since Exposure", subtitle="in ACS Cohort") + theme(plot.title = element_text(size=12, face="plain", hjust=0.5),
plot.subtitle=element_text(size=11, face="plain", hjust=0.5)) +
geom_signif(comparisons=list(c("Control", "Progressor")),
annotations = c("p = 1.0"), vjust=-0.4,
tip_length=0, y_position=1.075) + theme(panel.background = element_rect(fill = "white", colour = "white", size = 4)) +
scale_y_continuous(breaks=c(0.00, 0.25, 0.50, 0.75, 1.00), limits=c(0, 1.15)) +
theme(axis.title.x=element_blank())
ACS.exposure.plot
Reproduce Analysis for Figures 4B-D¶
- This requires the code from Figure 3 to have been run beforehand
Load and co-process the data from the ARCHS4 resource for GC6-74 and Singhania et al 2018¶
To download Singhania et al 2018 data from the ARCHS4 resource:¶
- These data are more difficult to extract due to GSE107995 being a multi-series record and not available by direct quiery at https://amp.pharm.mssm.edu/archs4/data.html
- We extracted these data from the entire gene expression table of the ARCHS4 resource:
- Run download_ARCHS4_datatable.sh to download human_matrix.h5 v6 Date: 9/2018. Our analyses should be unchanged if the matrix is updated on the ARCHS4 website.
- Run code in extract_ARCHS4_Singhania_etal.ipynb
- The ARCHS4 data for GSE94438 (GC6-74) from Figure 4A are also required
In [19]:
source("utils_submission.R")
In [11]:
Sul.path = paste(path, "/data/GSE94438", sep="")
Sul.pheno.set = getGEO(filename=paste(Sul.path, "GSE94438_series_matrix.txt.gz", sep="/"),
destdir=Sul.path)
Sul.pheno = filter.human.pheno(pData(Sul.pheno.set))
# Common names for progressor
Sul.pheno$group = ifelse(Sul.pheno$group == "Control", "control", "progressor")
Sul.exprs = read.table(paste(Sul.path, "gene_count_GSE94438.tsv", sep="/"), header=T, row.names=1)
Sul.exprs = filter.HUMAN.exprs(Sul.exprs, Sul.pheno)
Parsed with column specification: cols( .default = col_character() ) See spec(...) for full column specifications. Using locally cached version of GPL11154 found here: ./data/GSE94438/GPL11154.soft Warning message in filter.human.pheno(pData(Sul.pheno.set)): “NAs introduced by coercion”Warning message in filter.human.pheno(pData(Sul.pheno.set)): “NAs introduced by coercion”Warning message in filter.human.pheno(pData(Sul.pheno.set)): “NAs introduced by coercion”
age code gender group site subjectid time.from.exposure.months
GSM2475704 NA 672 NA NA NA NA NA
GSM2475705 NA 694 NA NA NA NA NA
GSM2475706 NA 695 NA NA NA NA NA
GSM2475722 NA 994 NA NA NA NA NA
GSM2475742 NA 1061 NA NA NA NA NA
GSM2475748 NA 1194 NA NA NA NA NA
time.to.tb.months
GSM2475704 NA
GSM2475705 NA
GSM2475706 NA
GSM2475722 NA
GSM2475742 NA
GSM2475748 NA
[1] "GSM2475598" "GSM2475603" "GSM2475587" "GSM2475592" "GSM2475599"
[6] "GSM2475604" "GSM2475360" "GSM2475387" "GSM2475362" "GSM2475389"
[11] "GSM2475402" "GSM2475555" "GSM2475361" "GSM2475388" "GSM2475359"
[16] "GSM2475390" "GSM2475588" "GSM2475593" "GSM2475591" "GSM2475596"
[21] "GSM2475589" "GSM2475594" "GSM2475590" "GSM2475595" "GSM2475597"
[26] "GSM2475602" "GSM2475579" "GSM2475601" "GSM2475600" "GSM2475605"
[31] "GSM2475403" "GSM2475554"
[1] 128 128 195 195 217 217 284 284 320 320 45 45 562 562 632 632 740 740 744
[20] 744 746 746 806 806 844 844 897 897 904 904 96 96
418 Levels: 1002 1007 1009 1010 1011 1014 1019 1020 1027 1028 1029 1030 ... 1194
[1] "15"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "72"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "92"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "99"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "358"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "376"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "524"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "729"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "730"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1066"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1114"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1116"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1231"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1233"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "5319"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "5358"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "5360"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "5365"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "08/G329"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "08/G568"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "08/G595"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "09/G131"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "09/G168"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "09/G238"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "09/G403"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "ARHHC16"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "DZHHC38"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "DZHHC84"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KAZHHC50"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KFHHC15"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KHHC04"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KHHC32"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KHHC36"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KHHC41"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "LDHHC18"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "MESHHC04"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "TEKHHC10"
[1] Test
84 Levels: 07/G123 07/G225 07/G437 07/G438 07/G468 08/G245 08/G249 ... W23HHC132
[1] "TEKHHC74"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "W23HHC15"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "W23HHC21"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "W23HHC29"
[1] Test
84 Levels: 07/G123 07/G225 07/G437 07/G438 07/G468 08/G245 08/G249 ... W23HHC132
[1] "W23HHC61"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1115"
[1] Test
21 Levels: 10 11 14 15 18 19 2008 2010 21 23 24 5 6 7 8 9 characterists ... Training
[1] "10/G289"
[1] Test
Levels: 19 21 22 24 failed for QC Test Training
[1] "DZHHC96"
[1] Test
21 Levels: 10 11 14 15 18 19 2008 2010 21 23 24 5 6 7 8 9 characterists ... Training
[1] "Subjects not assigned a training-test set that are now training"
09/G455 09/G476 LDHHC10 91420103 91451104 92245
2 2 1 1 1 1
[1] "about to return new data frame"
[1] "Identical column and rownames between exprs and pheno tables?"
[1] TRUE
In [20]:
Garra.path = paste(path, "/data/GSE107995", sep="")
Garra.pheno.set = getGEO(filename=paste(Garra.path, "GSE107995_series_matrix.txt.gz", sep="/"),
destdir=Garra.path)
Garra.pheno = filter.Garra.pheno(pData(Garra.pheno.set))
Parsed with column specification: cols( .default = col_character() ) See spec(...) for full column specifications. Using locally cached version of GPL20301 found here: ./data/GSE107995/GPL20301.soft Warning message in filter.Garra.pheno(pData(Garra.pheno.set)): “NAs introduced by coercion”
title
Berry_London_Sample1 : 1
Berry_London_Sample10: 1
Berry_London_Sample11: 1
Berry_London_Sample12: 1
Berry_London_Sample13: 1
Berry_London_Sample14: 1
(Other) :408
source_name_ch1 age_at_baseline_visit
Longitudnal_Leicester_Control_Non_progressor:69 Min. :16.00
Longitudnal_Leicester_LTBI_Non_progressor :69 1st Qu.:30.00
Leicester_Active_TB :53 Median :39.00
Leicester_Control :50 Mean :39.21
Leicester_LTBI :49 3rd Qu.:46.00
Berry_SouthAfrica_Validation_set_LTBI :31 Max. :84.00
(Other) :93 NA's :101
birth_place ethnicity gender
Foreign_Born:263 South_Asia_ISC :198 F :111
UK_Born : 50 British_Indian : 29 M :202
NA's :101 British : 21 NA's:101
East_African_Kenya : 13
Central_Asia_Afganistan: 8
(Other) : 44
NA's :101
group outlier patient_id smear_result
Active_TB : 90 No :364 Patient_035: 8 Negative:208
Control :131 Yes: 50 Patient_012: 7 Positive:105
LTBI :170 Patient_039: 7 NA's :101
LTBI_Progressor: 23 Patient_040: 7
Patient_047: 7
Patient_103: 7
(Other) :371
tb_disease_type timepoint_months uk_arrival_year
Contact_Non-Pulmonary:125 Baseline:191 Min. :1961
Contact_Pulmonary :135 1.1-2.0 : 27 1st Qu.:2000
Non-Pulmonary : 10 3.1-4.0 : 22 Median :2008
Pulmonary : 43 0.6-1.0 : 16 Mean :2004
NA's :101 4.1-5.0 : 12 3rd Qu.:2012
(Other) : 45 Max. :2016
NA's :101 NA's :151
visit_date time.since.exposure.days
Length:414 Min. :-24.00
Class :character 1st Qu.: 0.00
Mode :character Median : 0.00
Mean : 45.11
3rd Qu.: 76.00
Max. :412.00
NA's :104
In [21]:
dim(Garra.pheno)
- 414
- 15
In [22]:
Garra.exprs = read.table(paste(Garra.path, "Singhania_et_al_expression_ARCHS4.csv", sep="/"), header=T, row.names=1, sep=",")
In [23]:
dim(Garra.exprs)
- 35238
- 414
In [25]:
all.exprs = cbind(Garra.exprs, Sul.exprs)
dim(all.exprs)
- 35238
- 829
In [27]:
# Filter out genes whose counts are <= 5 in 50% of samples
exprs.j.keep = apply(all.exprs <= 5, 1, mean) <= 0.5
exprs.fil = all.exprs[exprs.j.keep,]
exprs.qn = as.data.frame(normalize.quantiles(as.matrix(exprs.fil)))
colnames(exprs.qn) = colnames(exprs.fil)
rownames(exprs.qn) = rownames(exprs.fil)
exprs.log = log2(exprs.qn + 1)
boxplot(log2(all.exprs+ 1), ylim=c(4,14))
boxplot(log2(exprs.fil+ 1), ylim=c(4,14))
boxplot(log2(exprs.qn+ 1), ylim=c(4,14))
In [28]:
Garra.exprs.log = exprs.log[, colnames(exprs.log) %in% row.names(Garra.pheno)]
Sul.exprs.log = exprs.log[, colnames(exprs.log) %in% row.names(Sul.pheno)]
identical(colnames(Garra.exprs.log), row.names(Garra.pheno))
TRUE
Repeat process of Figure 3B to build a model from GC6-74 data to predict time since TB exposure¶
In [29]:
Sul.pheno.6mo = Sul.pheno
# Randomly sample 50% of AHRI site to training set and test set for this new Sul.pheno
AHRI.subj = unique(dplyr::filter(Sul.pheno, site == "AHRI")$subjectid)
set.seed(100)
AHRI.subj.train = sample(AHRI.subj, length(AHRI.subj) / 2)
Sul.pheno.6mo$dataset[Sul.pheno.6mo$subjectid %in% AHRI.subj.train] = "Training"
# dplyr::filter pheno and expression table to include only AHRI and MRC sites and to include only 0 and 6 month time points
Sul.pheno.6mo = droplevels(Sul.pheno.6mo[Sul.pheno.6mo$site %in% c("AHRI", "MRC") & Sul.pheno.6mo$time.from.exposure.months %in% c(0, 6),])
Sul.exprs.log.6mo = Sul.exprs.log[, colnames(Sul.exprs.log) %in% row.names(Sul.pheno.6mo)]
Sul.pheno.6mo.train = droplevels(Sul.pheno.6mo[ Sul.pheno.6mo$dataset == "Training",])
Sul.pheno.6mo.test = droplevels(Sul.pheno.6mo[ Sul.pheno.6mo$dataset != "Training",])
Sul.exprs.train = t(Sul.exprs.log.6mo[,row.names(Sul.pheno.6mo.train)])
Sul.exprs.test = t(Sul.exprs.log.6mo[, row.names(Sul.pheno.6mo.test)])
set.seed(100)
folds.6mo = groupKFold(Sul.pheno.6mo.train$subjectid, k=10)
for (fold in lapply(folds.6mo, function(x) {Sul.pheno.6mo.train$subjectid[x]}))
print(length((as.character(fold))))
lapply(folds.6mo, function(x, y) table(y[x]), y = Sul.pheno.6mo.train$subjectid)
time.period.from.exposure = as.factor(ifelse(Sul.pheno.6mo.train$time.from.exposure.months == "0", "early", "late"))
test.time.period.from.exposure = as.factor(ifelse(Sul.pheno.6mo.test$time.from.exposure.months == "0", "early", "late"))
[1] 112 [1] 107 [1] 98 [1] 99 [1] 101 [1] 103 [1] 102 [1] 103 [1] 105 [1] 105
$Fold01
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 0 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 1 2 0
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 0 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 1
$Fold02
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 0 1 1 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 0 1 1 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 0 1 1 0
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 0 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
0 1 1 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 1
$Fold03
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 0 1 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 0 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
0 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 1 0 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
0 1 1 2 0 0 2 0
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 0 1 0 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 0 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 1 0
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 0 1 1
$Fold04
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
0 1 1 1 1 1 1 0
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 1 0 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 0 0 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
0 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
0 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 0 0 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 0 1 1 2 2 2 0
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
0 1 1 1 0 1
$Fold05
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 0 0 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
0 1 1 1 1 1 0 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 0 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 0 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
0 1 0 0 2 2 0 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
0 1 2 2 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 0
$Fold06
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 0 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
0 1 1 1 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 0 1 0 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 0 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 0 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 0 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
0 1 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 0 2 2 1 0 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 1
$Fold07
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 0 1 1 2 2 0
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 0 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 0 1 2 0 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 0 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 0 1 0
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 0 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 0 0 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 1
$Fold08
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 1 2 0 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 0 1 0
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 0 1 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 1 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 0 1 1 1 0 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 1 0 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 0 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 0 1 1 1 1
$Fold09
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 1 1 0 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 0 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 0 1 1 1 1
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 1 1 0 2 1 1 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 0 2 0 1 1 1 0
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 1 1 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 1 1 2 0 1 0 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 2 2 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 1 1 1 1
$Fold10
07/G189 07/G274 07/G277 07/G300 07/G351 07/G354 07/G368 07/G370
1 1 1 1 1 1 1 1
07/G419 08/G136 08/G141 08/G160 08/G245 08/G260 08/G324 08/G333
1 1 1 1 1 2 2 1
08/G343 08/G367 08/G400 08/G407 08/G415 08/G423 08/G424 08/G433
1 2 1 1 1 1 1 2
08/G493 08/G494 08/G541 08/G587 08/G596 08/G606 08/G613 08/G643
1 1 1 1 1 1 1 0
08/G652 08/G654 08/G695 08/G701 08/G703 08/G714 08/G715 08/G726
2 0 1 1 2 1 0 1
08/G782 08/G788 08/G797 08/G800 08/G810 08/G814 08/G893 09/G120
1 1 2 1 1 1 1 1
09/G133 09/G135 09/G179 09/G188 09/G196 09/G202 09/G204 09/G223
1 1 1 2 1 2 2 2
09/G224 09/G228 09/G247 09/G367 09/G371 09/G377 09/G388 09/G389
1 1 1 2 2 1 0 1
09/G390 09/G435 09/G442 09/G445 09/G455 09/G476 09/G477 09/G497
1 1 1 1 2 2 2 1
09/G524 10/G139 10/G178 10/G215 10/G407 DZHHC26 DZHHC69 KFHHC21
1 0 1 2 1 1 2 1
KHHC05 KHHC121 KHHC151 KHHC26 MOHHC04 MOHHC21 TEKHHC04 TEKHHC105
1 1 0 0 1 2 1 1
W23HHC03 W23HHC04 W23HHC08 W23HHC132 W23HHC58 W23HHC78
1 1 0 1 1 1
In [30]:
start_time <- Sys.time()
cluster = makeCluster(detectCores()-3) # Leaving 3 for other jobs
registerDoParallel(cluster)
print("Made the clusters")
myFunc <- caretSBF
myFunc$summary <- twoClassSummary
myFunc$score <- function(x, y) {
out <- wilcox.test(x ~ y)$p.value # Will have warnings due to ties; ignore for now.
out
}
filtercontrol.lm = sbfControl(functions = myFunc, method = "cv", index=folds.6mo, allowParallel=TRUE)
set.seed(10)
wilcoxWithFilter.lm = sbf(Sul.exprs.train, as.factor(Sul.pheno.6mo.train$time.from.exposure.months), sbfControl = filtercontrol.lm)
print("Did the feature selection")
stopCluster(cluster)
registerDoSEQ()
end_time <- Sys.time()
print(end_time - start_time)
[1] "Made the clusters" [1] "Did the feature selection" Time difference of 11.56077 mins
In [32]:
opt.Sul.Garra.vars = wilcoxWithFilter.lm$optVariables
In [33]:
set.seed(100)
n = 1000
lambda.grid = c(10 ^ runif(n, min = log10(1e-6), max = log10(1e2)))
alpha.grid = runif(length(lambda.grid), min = 0.00, 1.00)
train.grid = data.frame(lambda = sample(lambda.grid, length(lambda.grid)),
alpha = sample(alpha.grid, length(lambda.grid)))
In [34]:
seed=7
start_time <- Sys.time()
cluster = makeCluster(detectCores()-3) # Leaving 3 for other jobs
registerDoParallel(cluster)
methods = c("glmnet")
models = list()
control <- trainControl(method="cv", index=folds.6mo, savePredictions = 'final', allowParallel=TRUE,
classProbs=TRUE, summaryFunction=twoClassSummary) # Use AUC to pick the best model
for (alg in methods) {
set.seed(seed)
print("I have gotten to model:")
print(alg)
model = train(Sul.exprs.train[, colnames(Sul.exprs.train) %in% c(opt.Sul.Garra.vars)],
time.period.from.exposure,
method=alg, tuneGrid = train.grid, trControl=control,
metric="ROC")
models[[alg]] = model
}
stopCluster(cluster)
registerDoSEQ()
end_time <- Sys.time()
print(end_time - start_time)
[1] "I have gotten to model:" [1] "glmnet"
Warning message in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : “There were missing values in resampled performance measures.”
Time difference of 1.947424 mins
In [35]:
glmres = models$glmnet$results
graph.hyper (x=glmres$alpha, y=log10(glmres$lambda), z=glmres$ROC)
In [36]:
human.expose.Sul.Garra.model = models$glmnet
glmnet.Sul.val.ROC = my.roc(human.expose.Sul.Garra.model$pred$early, human.expose.Sul.Garra.model$pred$obs, "early")
pred.Sul.test.prob = predict(human.expose.Sul.Garra.model, newdata = Sul.exprs.test[, colnames(Sul.exprs.test) %in% c(opt.Sul.Garra.vars)], type="prob")
glmnet.Sul.test.ROC <- my.roc(pred.Sul.test.prob$early,
test.time.period.from.exposure,
"early", title="Glmnet ROC")
[1] "This is the AUC:" Area under the curve: 0.8831 [1] "This is the AUC p-value:" [1] 1.428919e-12 [1] "This is the AUC 95% Confidence Interval" 95% CI: 0.8185-0.9477 (DeLong) [1] "This is the AUC:" Area under the curve: 0.6565 [1] "This is the AUC p-value:" [1] 0.01342422 [1] "This is the AUC 95% Confidence Interval" 95% CI: 0.5255-0.7875 (DeLong)
Load 18 month time points from GC6-74 cohort¶
- Requires results from Figure 3B
In [48]:
# This is the GC6-74 data processed only by itself, from Figure 3B
dim(human.exprs.log)
- 14842
- 415
In [49]:
human.pheno.gam.eth = human.pheno
human.pheno.gam.eth.18mo = droplevels(dplyr::filter(human.pheno.gam.eth, site %in% c("AHRI", "MRC"), time.from.exposure.months %in% c(18)))
human.exprs.log.gam.eth.18mo = human.exprs.log[, colnames(human.exprs.log) %in% human.pheno.gam.eth.18mo$code]
human.exprs.log.gam.eth.18mo = t(human.exprs.log.gam.eth.18mo)
human.pheno.sun = human.pheno
human.pheno.sun.18mo = droplevels(dplyr::filter(human.pheno.sun, site %in% c("SUN"), time.from.exposure.months %in% c(18)))
human.exprs.log.sun.18mo = human.exprs.log[, colnames(human.exprs.log) %in% human.pheno.sun.18mo$code]
human.exprs.log.sun.18mo = t(human.exprs.log.sun.18mo)
table(human.pheno.gam.eth.18mo$time.from.exposure.months)
table(human.pheno.sun.18mo$time.from.exposure.months)
18 34
18 30
Use GC6-74 0 vs 6 months post-exposure model to predict time since exposure in GC6-74 18 month time points and in the Singhania et al cohorts¶
In [50]:
# optvars are the Wilcox filtered genes from Figure 3B
pred.human.exposure.gam.eth.18mo = predict(human.exposure.model, newdata = human.exprs.log.gam.eth.18mo[, colnames(human.exprs.log.gam.eth.18mo) %in% c(optvars)])
pred.human.exposure.sun.18mo = predict(human.exposure.model, newdata = human.exprs.log.sun.18mo[, colnames(human.exprs.log.sun.18mo) %in% c(optvars)])
ratio.gam.eth = sum(pred.human.exposure.gam.eth.18mo == "late") / length(pred.human.exposure.gam.eth.18mo)
ratio.sun = sum(pred.human.exposure.sun.18mo == "late") / length(pred.human.exposure.sun.18mo)
In [51]:
ratio.gam.eth
ratio.sun
0.529411764705882
0.166666666666667
In [52]:
pred.Garra.expose = predict(human.expose.Sul.Garra.model, newdata = t(Garra.exprs.log)[, rownames(Garra.exprs.log) %in% c(opt.Sul.Garra.vars)])
Garra.pheno$time.pred = pred.Garra.expose
table(Garra.pheno$time.pred, Garra.pheno$source_name_ch1)
Garra.pheno.contact = Garra.pheno[Garra.pheno$source_name_ch1 %in% c("Longitudnal_Leicester_Control_Non_progressor",
"Longitudnal_Leicester_LTBI_Non_progressor"),]
Garra.pheno.contact.ctrl = Garra.pheno[Garra.pheno$source_name_ch1 == "Longitudnal_Leicester_Control_Non_progressor",]
Garra.pheno.contact.ltbi = Garra.pheno[Garra.pheno$source_name_ch1 == "Longitudnal_Leicester_LTBI_Non_progressor",]
Garra.pheno.contact.ltbi.pos = dplyr::filter(Garra.pheno.contact.ltbi, patient_id %in% dplyr::filter(Garra.pheno.contact.ltbi, time.pred == "late")$patient_id)
Garra.pheno.contact.ctrl.pos = dplyr::filter(Garra.pheno.contact.ctrl, patient_id %in% dplyr::filter(Garra.pheno.contact.ctrl, time.pred == "late")$patient_id)
ratio.UK.ctrl = dim(dplyr::filter(Garra.pheno,source_name_ch1 =="Berry_London_Test_set_Control", time.pred == "late" ))[1] / dim(dplyr::filter(Garra.pheno,source_name_ch1 =="Berry_London_Test_set_Control" ))[1]
ratio.UK.SA.ltbi = dim(dplyr::filter(Garra.pheno,source_name_ch1 %in% c("Berry_London_Test_set_LTBI", "Berry_SouthAfrica_Validation_set_LTBI"), time.pred == "late" ))[1] / dim(dplyr::filter(Garra.pheno,source_name_ch1 %in% c("Berry_London_Test_set_LTBI", "Berry_SouthAfrica_Validation_set_LTBI") ))[1]
ratio.UK.SA.Leicester.activeTB = dim(dplyr::filter(Garra.pheno,source_name_ch1 %in% c("Berry_London_Test_set_Active_TB", "Berry_SouthAfrica_Validation_set_Active_TB", "Leicester_Active_TB"), time.pred == "late" ))[1] /
dim(dplyr::filter(Garra.pheno,source_name_ch1 %in% c("Berry_London_Test_set_Active_TB", "Berry_SouthAfrica_Validation_set_Active_TB", "Leicester_Active_TB") ))[1]
ratio.Leicester.progTB = dim(dplyr::filter(Garra.pheno,source_name_ch1 %in% c("Longitudnal_Leicester_LTBI_Progressor"), time.pred == "late" ))[1] /
dim(dplyr::filter(Garra.pheno,source_name_ch1 %in% c("Longitudnal_Leicester_LTBI_Progressor") ))[1]
long.subj = unique(dplyr::filter(Garra.pheno,source_name_ch1 %in% c("Longitudnal_Leicester_LTBI_Non_progressor", "Longitudnal_Leicester_Control_Non_progressor"))$patient_id)
ratio.Leicester.ctrl = dim(dplyr::filter(Garra.pheno,source_name_ch1 =="Leicester_Control", !(patient_id %in% long.subj), time.pred == "late" ))[1] / dim(dplyr::filter(Garra.pheno,source_name_ch1 =="Leicester_Control", !(patient_id %in% long.subj), ))[1]
ratio.Leicester.ltbi = dim(dplyr::filter(Garra.pheno,source_name_ch1 =="Leicester_LTBI", !(patient_id %in% long.subj), time.pred == "late" ))[1] / dim(dplyr::filter(Garra.pheno,source_name_ch1 =="Leicester_LTBI", !(patient_id %in% long.subj), ))[1]
ratio.Leicester.long.ctrl = dim(dplyr::filter(Garra.pheno,source_name_ch1 =="Longitudnal_Leicester_Control_Non_progressor", time.pred == "late" ))[1] / dim(dplyr::filter(Garra.pheno,source_name_ch1 =="Longitudnal_Leicester_Control_Non_progressor" ))[1]
ratio.Leicester.long.ltbi = dim(dplyr::filter(Garra.pheno,source_name_ch1 =="Longitudnal_Leicester_LTBI_Non_progressor", time.pred == "late" ))[1] / dim(dplyr::filter(Garra.pheno,source_name_ch1 =="Longitudnal_Leicester_LTBI_Non_progressor" ))[1]
Berry_London_Test_set_Active_TB Berry_London_Test_set_Control
early 21 12
late 0 0
Berry_London_Test_set_LTBI Berry_SouthAfrica_Validation_set_Active_TB
early 21 16
late 0 0
Berry_SouthAfrica_Validation_set_LTBI Leicester_Active_TB
early 31 53
late 0 0
Leicester_Control Leicester_LTBI
early 50 49
late 0 0
Longitudnal_Leicester_Control_Non_progressor
early 66
late 3
Longitudnal_Leicester_LTBI_Non_progressor
early 57
late 12
Longitudnal_Leicester_LTBI_Progressor
early 23
late 0
In [53]:
dim(dplyr::filter(Garra.pheno,source_name_ch1 =="Berry_London_Test_set_Control" ))[1]
dim(dplyr::filter(Garra.pheno,source_name_ch1 %in% c("Berry_London_Test_set_LTBI", "Berry_SouthAfrica_Validation_set_LTBI") ))[1]
dim(dplyr::filter(Garra.pheno,source_name_ch1 %in% c("Berry_London_Test_set_Active_TB", "Berry_SouthAfrica_Validation_set_Active_TB", "Leicester_Active_TB") ))[1]
dim(dplyr::filter(Garra.pheno,source_name_ch1 %in% c("Longitudnal_Leicester_LTBI_Progressor") ))[1]
dim(dplyr::filter(Garra.pheno,source_name_ch1 =="Leicester_Control", !(patient_id %in% long.subj), ))[1]
dim(dplyr::filter(Garra.pheno,source_name_ch1 =="Leicester_LTBI", !(patient_id %in% long.subj), ))[1]
dim(dplyr::filter(Garra.pheno,source_name_ch1 =="Longitudnal_Leicester_Control_Non_progressor" ))[1]
dim(dplyr::filter(Garra.pheno,source_name_ch1 =="Longitudnal_Leicester_LTBI_Non_progressor" ))[1]
dim(Garra.pheno)
12
52
90
23
35
33
69
69
- 414
- 16
In [54]:
ratio.UK.ctrl
ratio.UK.SA.ltbi
ratio.UK.SA.Leicester.activeTB
ratio.Leicester.progTB
ratio.Leicester.ctrl
ratio.Leicester.ltbi
ratio.Leicester.long.ctrl
ratio.Leicester.long.ltbi
0
0
0
0
0
0
0.0434782608695652
0.173913043478261
In [55]:
cohort = factor(c("GC6-74 18 mo\n(Gambia, Ethiopia)", "GC6-74 18 mo\n(South Africa)",
"Uninfected Control\n(London)", "LTBI\n(London, South Africa)",
"Active TB\n(London, South Africa, Leicester)", "TB Progressor\n(Leicester)",
"IGRA- contact\n(Leicester, baseline only)", "IGRA+ contact\n(Leicester, baseline only)",
"IGRA- longitudinal contact\n(Leicester)", "IGRA+ longitudinal contact\n(Leicester)" ) ,
ordered=TRUE,
levels = c("GC6-74 18 mo\n(Gambia, Ethiopia)", "GC6-74 18 mo\n(South Africa)",
"Uninfected Control\n(London)", "LTBI\n(London, South Africa)",
"Active TB\n(London, South Africa, Leicester)", "TB Progressor\n(Leicester)",
"IGRA- contact\n(Leicester, baseline only)", "IGRA+ contact\n(Leicester, baseline only)",
"IGRA- longitudinal contact\n(Leicester)", "IGRA+ longitudinal contact\n(Leicester)" ))
prop.6mo = c(ratio.gam.eth, ratio.sun, ratio.UK.ctrl, ratio.UK.SA.ltbi, ratio.UK.SA.Leicester.activeTB,
ratio.Leicester.progTB, ratio.Leicester.ctrl, ratio.Leicester.ltbi, ratio.Leicester.long.ctrl ,
ratio.Leicester.long.ltbi )
recent.contact.validation = data.frame(cohort=cohort, prop = prop.6mo)
Figure 4B¶
In [56]:
validate.recent.exposure.plot = ggplot(recent.contact.validation, aes(x=cohort, y=prop)) + geom_bar(stat="identity") +
labs(y="Proportion of Samples\nPredicted as 6 Month\nTime Point Post-Exposure") +
ggtitle("Predicted Time Since Active TB Exposure", subtitle="in GC6-74, Berry et al and Leicester cohorts") + theme(plot.title = element_text(size=12, face="plain", hjust=0.5),
plot.subtitle=element_text(size=11, face="plain", hjust=0.5)) +
theme(axis.text.x=element_text(angle=45, hjust=1, size = 7)) +
scale_y_continuous(breaks=c(0.00, 0.1, 0.2, 0.3, 0.4, 0.50)) +
theme(axis.title.x=element_blank(),
axis.title.y = element_text(size=9))
validate.recent.exposure.plot
Figure 4C¶
In [58]:
Garra.pheno.contact.ltbi.pos$time.pred.renamed = factor(ifelse(Garra.pheno.contact.ltbi.pos$time.pred == "early",
"baseline", "6 month post-exposure"),
ordered=TRUE,
levels = c("baseline" , "6 month post-exposure"))
pred.time.plot.pos = ggplot(Garra.pheno.contact.ltbi.pos, aes(time.since.exposure.days, time.pred.renamed, col=patient_id)) +
geom_point() +
geom_line(aes(time.since.exposure.days, time.pred.renamed, group=patient_id)) +
theme_bw() +
labs(x="Days Since Baseline Blood Collection", y="Model Prediction") + labs(color='Patient ID') + facet_wrap(~patient_id, ncol=2) + theme(
strip.text.x = element_blank(), strip.text.y = element_blank(), strip.background = element_blank()
) + ggtitle("Predicted Time Since Active TB Exposure", subtitle="in IGRA+ recent contacts from Leicester") + theme(plot.title = element_text(size=12, face="plain", hjust=0.5),
plot.subtitle=element_text(size=11, face="plain", hjust=0.5)) + # +
theme(axis.text.y = element_text(size=8),
axis.text.x = element_text(size=6),
legend.title = element_text(size=10),
legend.text = element_text(size=7))
pred.time.plot.pos
Figure 4D¶
In [59]:
Garra.pheno.contact.ctrl.pos$time.pred.renamed = factor(ifelse(Garra.pheno.contact.ctrl.pos$time.pred == "early",
"baseline", "6 month post-exposure"),
ordered=TRUE,
levels = c("baseline" , "6 month post-exposure"))
pred.time.plot.pos.ctrl = ggplot(Garra.pheno.contact.ctrl.pos, aes(time.since.exposure.days, time.pred.renamed, col=patient_id)) +
geom_point() +
geom_line(aes(time.since.exposure.days, time.pred.renamed, group=patient_id)) +
theme_bw() +
labs(x="Days Since Baseline Blood Collection", y="Model Prediction") + labs(color='Patient ID') + facet_wrap(~patient_id, ncol=2) + theme(
strip.text.x = element_blank(), strip.text.y = element_blank(), strip.background = element_blank()
) + ggtitle("Predicted Time Since Active TB Exposure", subtitle="in IGRA- recent contacts from Leicester") + theme(plot.title = element_text(size=12, face="plain", hjust=0.5),
plot.subtitle=element_text(size=11, face="plain", hjust=0.5)) + #+
coord_fixed(ratio = 100) +
theme(axis.text.y = element_text(size=8),
axis.text.x = element_text(size=6),
legend.title = element_text(size=10),
legend.text = element_text(size=7))
pred.time.plot.pos.ctrl
Reproduce Analysis for Figure 4E¶
Load data and filter probes by detection p values by processing the .idat files¶
In [208]:
if (!require("illuminaio")) {
source("https://bioconductor.org/biocLite.R")
biocLite("illuminaio")
library("illuminaio")
}
if (!require("limma")) {
source("https://bioconductor.org/biocLite.R")
biocLite("limma")
library("limma")
}
Loading required package: limma
Attaching package: ‘limma’
The following object is masked from ‘package:BiocGenerics’:
plotMA
In [215]:
source("utils_submission.R")
ado.path = paste(path, "/data/GSE116014", sep="")
ado.eset = getGEO(filename=paste(ado.path, "GSE116014_series_matrix.txt.gz", sep="/"),
destdir=ado.path)
Parsed with column specification: cols( .default = col_double(), ID_REF = col_character() ) See spec(...) for full column specifications. Using locally cached version of GPL10558 found here: data/GSE116014_adolescent//GPL10558.soft
In [214]:
idatfiles <- dir(ado.path, pattern="idat", full.names=T)
bgxfile <- dir(ado.path, pattern="B.txt.gz", full.names=T)
x <- read.idat(idatfiles, bgxfile[1])
x$other$Detection <- detectionPValues(x)
dim(x$other$Detection)
Reading manifest file /master/rault/TB/data/GSE116014/GPL10558_HumanHT-12_V4_0_R1_15002873_B.txt.gz ... Done /master/rault/TB/data/GSE116014/GSM3207067_8381645078_B_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207068_8381645078_C_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207069_8381645078_D_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207070_8381645078_E_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207071_8381645078_F_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207072_8381645078_G_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207073_8381645078_H_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207074_8381645078_I_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207075_8381645078_J_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207076_8381645078_K_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207077_8381645078_L_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207078_8381645088_A_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207079_8381645088_B_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207080_8381645088_C_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207081_8381645088_D_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207082_8381645088_E_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207083_8381645088_F_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207084_8381645088_G_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207085_8381645088_H_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207086_8381645088_I_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207087_8381645088_J_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207088_8381645088_K_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207089_8381645088_L_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207090_8381645095_A_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207091_8381645095_B_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207092_8381645095_C_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207093_8381645095_D_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207094_8381645095_E_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207095_8381645095_F_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207096_8381645095_G_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207097_8381645095_H_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207098_8381645095_I_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207099_8381645095_J_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207100_8381645095_K_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207101_8381645095_L_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207102_8381645096_A_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207103_8381645096_B_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207104_8381645096_C_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207105_8381645096_D_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207106_8381645096_E_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207107_8381645096_F_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207108_8381645096_G_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207109_8381645096_H_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207110_8381645096_I_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207111_8381645096_J_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207112_8381645096_K_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207113_8381645096_L_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207114_8292044004_A_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207115_8292044004_B_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207116_8292044004_C_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207117_8292044004_D_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207118_8292044004_E_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207119_8292044004_F_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207120_8292044004_G_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207121_8292044004_H_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207122_8292044004_I_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207123_8292044004_J_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207124_8292044004_K_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207125_8292044004_L_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207126_8381653006_B_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207127_8381653006_C_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207128_8381653006_D_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207129_8381653006_E_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207130_8381653006_F_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207131_8381653006_G_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207132_8381653006_H_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207133_8381653006_I_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207134_8381653006_J_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207135_8381653006_K_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207136_8381653006_L_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207137_8381688026_A_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207138_8381688026_B_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207139_8381688026_C_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207140_8381688026_D_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207141_8381688026_E_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207142_8381688026_F_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207143_8381688026_G_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207144_8381688026_H_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207145_8381688026_I_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207146_8381688026_J_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207147_8381688026_K_Grn.idat ... Done /master/rault/TB/data/GSE116014/GSM3207148_8381688026_L_Grn.idat ... Done Finished reading data.
- 48118
- 82
In [217]:
row.names(x$other$Detection) = x$genes$Probe_Id
df = x$other$Detection
probe = as.character(row.names(df))
df = data.frame(df, probe=probe)
df = df[!duplicated(df$probe),]
df = df[match(featureNames(ado.eset), df$probe),]
row.names(df) = df$probe
df$probe=NULL
print(identical(featureNames(ado.eset), row.names(df)))
Warning message in data.row.names(row.names, rowsi, i): “some row.names duplicated: 47233,47236,47238,47240,47242,47243,47246,47247,47251,47253,47255 --> row.names NOT used”
[1] TRUE
In [218]:
write.csv(df, paste(ado.path, "GSE116014_DetectionPValues.csv", sep="/"))
In [222]:
dPvalues = read.csv(paste(ado.path, "GSE116014_DetectionPValues.csv", sep="/"), header=T, row.names=1)
Prepare data for training a model to predict time since first known QFT conversion (0 vs 6 months)¶
In [224]:
det_pval_thresh = 0.01
# Choose genes expressed in 10% of samples
percent_samples= 0.10
PAL.10 = row.names(dPvalues)[apply(dPvalues <= det_pval_thresh, 1, mean) >= percent_samples]
ado.eset.PAL.10 = ado.eset[featureNames(ado.eset) %in% PAL.10,]
ado.exprs = exprs(ado.eset.PAL.10)
ado.pheno = pData(ado.eset.PAL.10)
colnames(ado.pheno) = gsub(" ",".", gsub(":ch1|\\(|\\)","",colnames(ado.pheno)))
ado.pheno = ado.pheno[,c('draw.day',
'latent.tb.status.at.blood.draw',
'patientid',
'relative.time.from.conversion.days',
'time.of.conversion.days',
'tissue')]
num.f = c ('draw.day',
'relative.time.from.conversion.days',
'time.of.conversion.days')
for (f in num.f) {
ado.pheno[,f] = as.numeric(ado.pheno[,f])
}
ado.exprs.qn = normalize.quantiles(ado.exprs)
row.names(ado.exprs.qn) = row.names(ado.exprs)
colnames(ado.exprs.qn) = colnames(ado.exprs)
ado.exprs.qn[ado.exprs.qn < 10] = 10
ado.exprs.log = log2(ado.exprs.qn)
cut = 0.6
filter.QR = function(x) {IQR(x) / median(x)}
genes.range = apply(ado.exprs.log, 1, filter.QR)
criteria = quantile(genes.range, c(1-cut))
genes.f = rownames(ado.exprs.log)[genes.range > criteria]
ado.exprs.var = ado.exprs.log[genes.f,]
ado.pheno.qft = ado.pheno[ado.pheno$relative.time.from.conversion.days %in% c(0,180),]
ntrain = 0.7 * length(unique(ado.pheno.qft$patientid))
ntest = 0.3 * length(unique(ado.pheno.qft$patientid))
set.seed(100)
train.patients = sample(unique(as.character(ado.pheno.qft$patientid)), ntrain)
test.patients = setdiff(unique(as.character(ado.pheno.qft$patientid)), train.patients)
print("Number of common patients between training and test set (should be 0)")
length(intersect(train.patients, test.patients))
ado.pheno.train = droplevels(ado.pheno.qft[ado.pheno.qft$patientid %in% train.patients,])
ado.pheno.test = droplevels(ado.pheno.qft[ado.pheno.qft$patientid %in% test.patients,])
ado.exprs.qft = ado.exprs.var[, colnames(ado.exprs.var) %in% row.names(ado.pheno.qft)]
ado.exprs.train = t(ado.exprs.qft[,ado.pheno.qft$patientid %in% train.patients])
ado.exprs.test = t(ado.exprs.qft[,ado.pheno.qft$patientid %in% test.patients])
set.seed(100)
folds.ado = groupKFold(ado.pheno.train$patientid, k=11)
for (fold in lapply(folds.ado, function(x) {ado.pheno.train$patientid[x]}))
print(length((as.character(fold))))
lapply(folds.ado, function(x, y) table(y[x]), y = ado.pheno.train$patientid)
time.since.QFT.conversion = as.factor(ifelse(ado.pheno.train$relative.time.from.conversion.days == "0", "early", "late"))
test.time.since.QFT.conversion = as.factor(ifelse(ado.pheno.test$relative.time.from.conversion.days == "0", "early", "late"))
[1] "Number of common patients between training and test set (should be 0)"
0
[1] 37 [1] 35 [1] 35 [1] 33 [1] 33 [1] 34 [1] 34 [1] 34 [1] 32 [1] 35
$Fold01
ID#: 04/0017 ID#: 04/0090 ID#: 04/0106 ID#: 04/0352 ID#: 04/0402 ID#: 04/0446
2 1 1 1 1 1
ID#: 04/0702 ID#: 04/0758 ID#: 04/0785 ID#: 04/0807 ID#: 04/0953 ID#: 04/0974
2 1 1 2 2 1
ID#: 04/1032 ID#: 04/1139 ID#: 04/1154 ID#: 04/1157 ID#: 04/1187 ID#: 04/1354
1 2 2 2 1 1
ID#: 07/0088 ID#: 07/0222 ID#: 07/0487 ID#: 07/0547 ID#: 07/0551 ID#: 07/0675
1 1 1 1 1 2
ID#: 07/0788 ID#: 07/0845 ID#: 07/0901
2 2 1
$Fold02
ID#: 04/0017 ID#: 04/0090 ID#: 04/0106 ID#: 04/0344 ID#: 04/0352 ID#: 04/0402
2 1 1 1 1 1
ID#: 04/0446 ID#: 04/0702 ID#: 04/0758 ID#: 04/0807 ID#: 04/0953 ID#: 04/0974
1 2 1 2 2 1
ID#: 04/1032 ID#: 04/1139 ID#: 04/1154 ID#: 04/1157 ID#: 04/1187 ID#: 04/1354
1 2 2 2 1 1
ID#: 07/0088 ID#: 07/0222 ID#: 07/0487 ID#: 07/0547 ID#: 07/0551 ID#: 07/0675
1 1 1 1 1 2
ID#: 07/0845 ID#: 07/0901
2 1
$Fold03
ID#: 04/0017 ID#: 04/0106 ID#: 04/0344 ID#: 04/0352 ID#: 04/0402 ID#: 04/0446
2 1 1 1 1 1
ID#: 04/0702 ID#: 04/0758 ID#: 04/0785 ID#: 04/0807 ID#: 04/0953 ID#: 04/0974
2 1 1 2 2 1
ID#: 04/1032 ID#: 04/1139 ID#: 04/1154 ID#: 04/1187 ID#: 04/1354 ID#: 07/0088
1 2 2 1 1 1
ID#: 07/0222 ID#: 07/0487 ID#: 07/0547 ID#: 07/0551 ID#: 07/0675 ID#: 07/0788
1 1 1 1 2 2
ID#: 07/0845 ID#: 07/0901
2 1
$Fold04
ID#: 04/0090 ID#: 04/0106 ID#: 04/0344 ID#: 04/0352 ID#: 04/0402 ID#: 04/0446
1 1 1 1 1 1
ID#: 04/0702 ID#: 04/0758 ID#: 04/0785 ID#: 04/0807 ID#: 04/0953 ID#: 04/1032
2 1 1 2 2 1
ID#: 04/1139 ID#: 04/1154 ID#: 04/1157 ID#: 07/0088 ID#: 07/0222 ID#: 07/0487
2 2 2 1 1 1
ID#: 07/0547 ID#: 07/0551 ID#: 07/0675 ID#: 07/0788 ID#: 07/0845 ID#: 07/0901
1 1 2 2 2 1
$Fold05
ID#: 04/0017 ID#: 04/0090 ID#: 04/0106 ID#: 04/0344 ID#: 04/0352 ID#: 04/0402
2 1 1 1 1 1
ID#: 04/0446 ID#: 04/0758 ID#: 04/0785 ID#: 04/0807 ID#: 04/0953 ID#: 04/0974
1 1 1 2 2 1
ID#: 04/1139 ID#: 04/1154 ID#: 04/1157 ID#: 04/1187 ID#: 04/1354 ID#: 07/0088
2 2 2 1 1 1
ID#: 07/0222 ID#: 07/0487 ID#: 07/0547 ID#: 07/0551 ID#: 07/0788 ID#: 07/0845
1 1 1 1 2 2
ID#: 07/0901
1
$Fold06
ID#: 04/0017 ID#: 04/0090 ID#: 04/0106 ID#: 04/0344 ID#: 04/0446 ID#: 04/0702
2 1 1 1 1 2
ID#: 04/0758 ID#: 04/0785 ID#: 04/0807 ID#: 04/0953 ID#: 04/0974 ID#: 04/1032
1 1 2 2 1 1
ID#: 04/1139 ID#: 04/1154 ID#: 04/1157 ID#: 04/1187 ID#: 04/1354 ID#: 07/0088
2 2 2 1 1 1
ID#: 07/0487 ID#: 07/0551 ID#: 07/0675 ID#: 07/0788 ID#: 07/0845 ID#: 07/0901
1 1 2 2 2 1
$Fold07
ID#: 04/0017 ID#: 04/0090 ID#: 04/0344 ID#: 04/0352 ID#: 04/0402 ID#: 04/0446
2 1 1 1 1 1
ID#: 04/0702 ID#: 04/0785 ID#: 04/0953 ID#: 04/0974 ID#: 04/1032 ID#: 04/1139
2 1 2 1 1 2
ID#: 04/1154 ID#: 04/1157 ID#: 04/1187 ID#: 04/1354 ID#: 07/0088 ID#: 07/0222
2 2 1 1 1 1
ID#: 07/0487 ID#: 07/0547 ID#: 07/0551 ID#: 07/0675 ID#: 07/0788 ID#: 07/0845
1 1 1 2 2 2
ID#: 07/0901
1
$Fold08
ID#: 04/0017 ID#: 04/0090 ID#: 04/0106 ID#: 04/0344 ID#: 04/0352 ID#: 04/0402
2 1 1 1 1 1
ID#: 04/0446 ID#: 04/0702 ID#: 04/0758 ID#: 04/0785 ID#: 04/0807 ID#: 04/0953
1 2 1 1 2 2
ID#: 04/0974 ID#: 04/1032 ID#: 04/1139 ID#: 04/1157 ID#: 04/1187 ID#: 04/1354
1 1 2 2 1 1
ID#: 07/0222 ID#: 07/0547 ID#: 07/0551 ID#: 07/0675 ID#: 07/0788 ID#: 07/0845
1 1 1 2 2 2
ID#: 07/0901
1
$Fold09
ID#: 04/0017 ID#: 04/0090 ID#: 04/0106 ID#: 04/0344 ID#: 04/0352 ID#: 04/0402
2 1 1 1 1 1
ID#: 04/0702 ID#: 04/0758 ID#: 04/0785 ID#: 04/0807 ID#: 04/0953 ID#: 04/0974
2 1 1 2 2 1
ID#: 04/1032 ID#: 04/1154 ID#: 04/1157 ID#: 04/1187 ID#: 04/1354 ID#: 07/0088
1 2 2 1 1 1
ID#: 07/0222 ID#: 07/0487 ID#: 07/0547 ID#: 07/0675 ID#: 07/0788 ID#: 07/0901
1 1 1 2 2 1
$Fold10
ID#: 04/0017 ID#: 04/0090 ID#: 04/0106 ID#: 04/0344 ID#: 04/0352 ID#: 04/0402
2 1 1 1 1 1
ID#: 04/0446 ID#: 04/0702 ID#: 04/0758 ID#: 04/0785 ID#: 04/0807 ID#: 04/0974
1 2 1 1 2 1
ID#: 04/1032 ID#: 04/1139 ID#: 04/1154 ID#: 04/1157 ID#: 04/1187 ID#: 04/1354
1 2 2 2 1 1
ID#: 07/0088 ID#: 07/0222 ID#: 07/0487 ID#: 07/0547 ID#: 07/0551 ID#: 07/0675
1 1 1 1 1 2
ID#: 07/0788 ID#: 07/0845
2 2
Train the model¶
In [225]:
set.seed(100)
n = 1000
lambda.grid = c(10 ^ runif(n, min = log10(1e-5), max = log10(1e2)))
alpha.grid = runif(length(lambda.grid), min = 0.00, 1.0)
train.grid = data.frame(lambda = sample(lambda.grid, length(lambda.grid)),
alpha = sample(alpha.grid, length(lambda.grid)))
In [226]:
seed=7
start_time <- Sys.time()
cluster = makeCluster(detectCores()-3) # Leaving 3 for other jobs
registerDoParallel(cluster)
methods = c("glmnet")
models = list()
control <- trainControl(method="cv", index=folds.ado, savePredictions='final', allowParallel=TRUE,
classProbs=TRUE, summaryFunction=twoClassSummary)
for (alg in methods) {
set.seed(seed)
print("I have gotten to model:")
print(alg)
model = train(ado.exprs.train,
as.factor(time.since.QFT.conversion),
method=alg, trControl=control, tuneGrid=train.grid,
metric="ROC")
models[[alg]] = model
}
stopCluster(cluster)
registerDoSEQ()
end_time <- Sys.time()
print(end_time - start_time)
[1] "I have gotten to model:" [1] "glmnet"
Warning message in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, : “There were missing values in resampled performance measures.”
Time difference of 4.250001 mins
In [232]:
glmres = models$glmnet$results
graph.hyper(x=glmres$alpha, y=log10(glmres$lambda), z=glmres$ROC)
In [228]:
human.QFT.model = models$glmnet
Figure 4E¶
In [229]:
glmnet.human.QFT.val.ROC = my.roc(human.QFT.model$pred$early, human.QFT.model$pred$obs, "early")
pred.human.QFT.test = predict(human.QFT.model, newdata = ado.exprs.test, type="prob")
glmnet.human.QFT.test.ROC = my.roc(pred.human.QFT.test$early,
test.time.since.QFT.conversion,
"early")
[1] "This is the AUC:" Area under the curve: 0.6387 [1] "This is the AUC p-value:" [1] 0.9282259 [1] "This is the AUC 95% Confidence Interval" 95% CI: 0.4577-0.8196 (DeLong) [1] "This is the AUC:" Area under the curve: 0.5444 [1] "This is the AUC p-value:" [1] 0.6401524 [1] "This is the AUC 95% Confidence Interval" 95% CI: 0.2654-0.8234 (DeLong)
In [230]:
human.QFT.plot = ggroc(list(CV=glmnet.human.QFT.val.ROC,
test=glmnet.human.QFT.test.ROC),
legacy.axes=TRUE) +
geom_abline(intercept = 0, slope = 1, color = "lightgrey", size = 0.25) +
ggtitle("0 vs 6 months post first QFT+") + theme(plot.title = element_text(size=12, face="plain")) +
scale_color_manual(name="M.tb Infected Adolescents",
labels=c("CV" =expression("CV: AUC 0.64, p = 0.92"),
"test"=expression("Test: AUC 0.54, p = 0.64")),
values=c("CV"="blue", "test"="red")) +
theme(legend.position=c(0.40,0.20), legend.title = element_text(size=10), legend.text = element_text(size=8))
human.QFT.plot
Figure 5 Human Analysis (Grand Challenges 6-74 cohort)¶
- This code reproduces the cell-type analysis of Figure 5, showing that "Time since TB exposure in humans is associated with alteration in CD4+ T cell proportion"
- The Ingenuity Pathway Analysis for Figure 5E is excluded (commercial software)
Load and Install Required Libraries¶
- If the whole notebook is run in one sitting, loading MetaIntegrator may give a
maximal number of DLLs reached...error. In this case, simply restart the notebook and run this Figure 5 code alone. It does not depend upon any other code in the notebook.
In [ ]:
path="." # this is the TB repository folder
In [92]:
if (!require("preprocessCore")) {
source("https://bioconductor.org/biocLite.R")
biocLite("preprocessCore")
library("preprocessCore")
}
source("https://bioconductor.org/biocLite.R")
if (!require("Biobase")) {
biocLite("Biobase")
library("Biobase")
}
if (!require("GEOquery")) {
biocLite("GEOquery")
library("GEOquery")
}
if (!require("ggplot2")) {
install.packages("ggplot2")
library("ggplot2")
}
if (!require("glmnet")) {
install.packages("glmnet")
library("glmnet")
}
if (!require("caret")) {
install.packages("caret")
library("caret")
}
if (!require("dplyr")) {
install.packages("dplyr")
library("dplyr")
}
if (!require("ggsignif")) {
install.packages("ggsignif")
library("ggsignif")
}
if (!require("doParallel")) {
install.packages("doParallel")
library("doParallel")
}
if (!require("cowplot")) {
install.packages("cowplot")
library("cowplot")
}
if (!require("pROC")) {
# pROC 1.12.0 is required, and may not be the default installation:
packageUrl<- "https://cran.r-project.org/src/contrib/Archive/pROC/pROC_1.12.0.tar.gz"
install.packages(packageUrl, repos=NULL, type='source')
library("pROC")
}
if (!require("lme4")) {
install.packages("lme4")
library("lme4")
}
if (!require("lmerTest")) {
install.packages("lmerTest")
library("lmerTest")
}
if (!require("MetaIntegrator")) {
# First install several dependencies required by MetaIntegrator 2.0.0
if (!require("multtest")) {
biocLite("multtest")
library("multtest")
}
if (!require("GEOmetadb")) {
biocLite("GEOmetadb")
library("GEOmetadb")
}
if (!require("biomaRt")) {
biocLite("biomaRt")
library("biomaRt")
}
install.packages(c("ggpubr", "ROCR", "pracma", "COCONUT" , "Metrics", "manhattanly", "snplist", "DT", "pheatmap", "HGNChelper"))
# Now install MetaIntegrator. Version 2.0.0 is required to use ImmunoStates
packageUrl = "https://cran.r-project.org/src/contrib/MetaIntegrator_2.0.0.tar.gz"
install.packages(packageUrl, repos=NULL, type='source')
library("MetaIntegrator")
}
Bioconductor version 3.6 (BiocInstaller 1.28.0), ?biocLite for help
A new version of Bioconductor is available after installing the most recent
version of R; see http://bioconductor.org/install
Loading required package: lmerTest
Attaching package: ‘lmerTest’
The following object is masked from ‘package:lme4’:
lmer
The following object is masked from ‘package:stats’:
step
Reproduce Analysis for Figures 5A-D¶
Load the data and perform cell-type deconvolution using Immunostates and linear mixed model analyses¶
In [65]:
source("utils_submission.R")
source("utils_immunoStates.R")
In [69]:
Sul.path = paste(path, "/data/GSE94438", sep="")
Sul.pheno.set = getGEO(filename=paste(Sul.path, "GSE94438_series_matrix.txt.gz", sep="/"),
destdir=Sul.path)
Sul.pheno = filter.human.pheno(pData(Sul.pheno.set))
Sul.exprs = read.table(paste(Sul.path, "gene_count_GSE94438.tsv", sep="/"), header=T, row.names=1)
Sul.exprs = filter.HUMAN.exprs(Sul.exprs, Sul.pheno)
Parsed with column specification: cols( .default = col_character() ) See spec(...) for full column specifications. Using locally cached version of GPL11154 found here: ./data/GSE94438/GPL11154.soft Warning message in filter.human.pheno(pData(Sul.pheno.set)): “NAs introduced by coercion”Warning message in filter.human.pheno(pData(Sul.pheno.set)): “NAs introduced by coercion”Warning message in filter.human.pheno(pData(Sul.pheno.set)): “NAs introduced by coercion”
age code gender group site subjectid time.from.exposure.months
GSM2475704 NA 672 NA NA NA NA NA
GSM2475705 NA 694 NA NA NA NA NA
GSM2475706 NA 695 NA NA NA NA NA
GSM2475722 NA 994 NA NA NA NA NA
GSM2475742 NA 1061 NA NA NA NA NA
GSM2475748 NA 1194 NA NA NA NA NA
time.to.tb.months
GSM2475704 NA
GSM2475705 NA
GSM2475706 NA
GSM2475722 NA
GSM2475742 NA
GSM2475748 NA
[1] "GSM2475598" "GSM2475603" "GSM2475587" "GSM2475592" "GSM2475599"
[6] "GSM2475604" "GSM2475360" "GSM2475387" "GSM2475362" "GSM2475389"
[11] "GSM2475402" "GSM2475555" "GSM2475361" "GSM2475388" "GSM2475359"
[16] "GSM2475390" "GSM2475588" "GSM2475593" "GSM2475591" "GSM2475596"
[21] "GSM2475589" "GSM2475594" "GSM2475590" "GSM2475595" "GSM2475597"
[26] "GSM2475602" "GSM2475579" "GSM2475601" "GSM2475600" "GSM2475605"
[31] "GSM2475403" "GSM2475554"
[1] 128 128 195 195 217 217 284 284 320 320 45 45 562 562 632 632 740 740 744
[20] 744 746 746 806 806 844 844 897 897 904 904 96 96
418 Levels: 1002 1007 1009 1010 1011 1014 1019 1020 1027 1028 1029 1030 ... 1194
[1] "15"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "72"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "92"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "99"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "358"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "376"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "524"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "729"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "730"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1066"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1114"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1116"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1231"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1233"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "5319"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "5358"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "5360"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "5365"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "08/G329"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "08/G568"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "08/G595"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "09/G131"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "09/G168"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "09/G238"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "09/G403"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "ARHHC16"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "DZHHC38"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "DZHHC84"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KAZHHC50"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KFHHC15"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KHHC04"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KHHC32"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KHHC36"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "KHHC41"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "LDHHC18"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "MESHHC04"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "TEKHHC10"
[1] Test
84 Levels: 07/G123 07/G225 07/G437 07/G438 07/G468 08/G245 08/G249 ... W23HHC132
[1] "TEKHHC74"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "W23HHC15"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "W23HHC21"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "W23HHC29"
[1] Test
84 Levels: 07/G123 07/G225 07/G437 07/G438 07/G468 08/G245 08/G249 ... W23HHC132
[1] "W23HHC61"
[1] Test
16 Levels: 09/G262 10/G261 2006 2007 2008 2009 F KHHC26 M Not R S ... unmatched
[1] "1115"
[1] Test
21 Levels: 10 11 14 15 18 19 2008 2010 21 23 24 5 6 7 8 9 characterists ... Training
[1] "10/G289"
[1] Test
Levels: 19 21 22 24 failed for QC Test Training
[1] "DZHHC96"
[1] Test
21 Levels: 10 11 14 15 18 19 2008 2010 21 23 24 5 6 7 8 9 characterists ... Training
[1] "Subjects not assigned a training-test set that are now training"
09/G455 09/G476 LDHHC10 91420103 91451104 92245
2 2 1 1 1 1
[1] "about to return new data frame"
[1] "Identical column and rownames between exprs and pheno tables?"
[1] TRUE
In [ ]:
Sul.pheno.6mo = Sul.pheno
# Randomly sample 50% of AHRI site to training set and test set for this new Sul.pheno
AHRI.subj = unique(dplyr::filter(Sul.pheno, site == "AHRI")$subjectid)
set.seed(100)
AHRI.subj.train = sample(AHRI.subj, length(AHRI.subj) / 2)
Sul.pheno.6mo$dataset[Sul.pheno.6mo$subjectid %in% AHRI.subj.train] = "Training"
# filter pheno and expression table to include only AHRI and MRC sites and to include only 0 and 6 month time points
Sul.pheno.6mo = droplevels(Sul.pheno.6mo[Sul.pheno.6mo$site %in% c("AHRI", "MRC") & Sul.pheno.6mo$time.from.exposure.months %in% c(0, 6),])
In [99]:
# Filter out genes whose counts are <= 5 in 50% of samples
exprs.j.keep = apply(Sul.exprs <= 5, 1, mean) <= 0.5
Sul.exprs.qn = as.data.frame(normalize.quantiles(as.matrix(Sul.exprs[exprs.j.keep,])))
colnames(Sul.exprs.qn) = colnames(Sul.exprs[exprs.j.keep,])
rownames(Sul.exprs.qn) = rownames(Sul.exprs[exprs.j.keep,])
Sul.decov.qn = iSdeconvolution(immunoStatesMatrix, Sul.exprs.qn)
Sul.decov.qn.data = as.data.frame(Sul.decov.qn[,!(colnames(Sul.decov.qn) %in% c('P-value', 'Correlation', 'RMSE')) ])
summary(Sul.decov.qn.data)
# Make everything on the percent scale.
Sul.decov.qn.data = Sul.decov.qn.data * 100
cells.Sul.6mo = data.frame(Sul.decov.qn.data[row.names(Sul.decov.qn.data) %in%
rownames(Sul.pheno.6mo),] ,
time.from.exposure.months = Sul.pheno.6mo$time.from.exposure.months,
patient = Sul.pheno.6mo$subjectid,
site = Sul.pheno.6mo$site)
cells.Sul.18mo = data.frame(Sul.decov.qn.data[row.names(Sul.decov.qn.data) %in%
rownames(Sul.pheno[Sul.pheno$time.from.exposure.months != 6,]),] ,
time.from.exposure.months = Sul.pheno[Sul.pheno$time.from.exposure.months != 6,]$time.from.exposure.months,
patient = Sul.pheno[Sul.pheno$time.from.exposure.months != 6,]$subjectid,
site = Sul.pheno[Sul.pheno$time.from.exposure.months != 6,]$site)
cells.Sul.6mo$NK_cell = cells.Sul.6mo$CD56bright_natural_killer_cell + cells.Sul.6mo$CD56dim_natural_killer_cell
cells.Sul.18mo$NK_cell = cells.Sul.18mo$CD56bright_natural_killer_cell + cells.Sul.18mo$CD56dim_natural_killer_cell
CD14_positive_monocyte CD16_positive_monocyte CD4_positive_alpha_beta_T_cell Min. :0.00000 Min. :0.00000 Min. :0.0287 1st Qu.:0.01157 1st Qu.:0.09289 1st Qu.:0.2290 Median :0.03401 Median :0.11809 Median :0.2811 Mean :0.04243 Mean :0.11914 Mean :0.2729 3rd Qu.:0.06088 3rd Qu.:0.14111 3rd Qu.:0.3155 Max. :0.22496 Max. :0.27652 Max. :0.4300 CD56bright_natural_killer_cell CD56dim_natural_killer_cell Min. :0.001581 Min. :0.0000000 1st Qu.:0.173857 1st Qu.:0.0000000 Median :0.201391 Median :0.0000000 Mean :0.196017 Mean :0.0001228 3rd Qu.:0.227082 3rd Qu.:0.0000000 Max. :0.302639 Max. :0.0305743 CD8_positive_alpha_beta_T_cell MAST_cell basophil Min. :0.00000 Min. :0.00000 Min. :0.000000 1st Qu.:0.03045 1st Qu.:0.00000 1st Qu.:0.000000 Median :0.04505 Median :0.00000 Median :0.000000 Mean :0.05184 Mean :0.00674 Mean :0.001395 3rd Qu.:0.06616 3rd Qu.:0.00783 3rd Qu.:0.001962 Max. :0.29926 Max. :0.07398 Max. :0.019922 eosinophil gamma_delta_T_cell hematopoietic_progenitor Min. :0.0000000 Min. :0 Min. :0 1st Qu.:0.0000000 1st Qu.:0 1st Qu.:0 Median :0.0000000 Median :0 Median :0 Mean :0.0003852 Mean :0 Mean :0 3rd Qu.:0.0000000 3rd Qu.:0 3rd Qu.:0 Max. :0.0369485 Max. :0 Max. :0 macrophage_m0 macrophage_m1 macrophage_m2 memory_B_cell Min. :0.0000000 Min. :0.000000 Min. :0.00000 Min. :0.0000000 1st Qu.:0.0000000 1st Qu.:0.000000 1st Qu.:0.02510 1st Qu.:0.0000000 Median :0.0000000 Median :0.000000 Median :0.03627 Median :0.0000000 Mean :0.0003148 Mean :0.001827 Mean :0.03363 Mean :0.0002578 3rd Qu.:0.0000000 3rd Qu.:0.000000 3rd Qu.:0.04543 3rd Qu.:0.0000000 Max. :0.0205051 Max. :0.200310 Max. :0.09057 Max. :0.0280780 myeloid_dendritic_cell naive_B_cell neutrophil Min. :0.0000000 Min. :0.00000 Min. :0.005796 1st Qu.:0.0000000 1st Qu.:0.06632 1st Qu.:0.110075 Median :0.0000000 Median :0.08986 Median :0.164966 Mean :0.0001102 Mean :0.09602 Mean :0.173338 3rd Qu.:0.0000000 3rd Qu.:0.11714 3rd Qu.:0.216300 Max. :0.0240314 Max. :0.26094 Max. :0.464572 plasma_cell plasmacytoid_dendritic_cell Min. :0.0000000 Min. :0.000000 1st Qu.:0.0000000 1st Qu.:0.000000 Median :0.0000000 Median :0.000000 Mean :0.0004163 Mean :0.003158 3rd Qu.:0.0000000 3rd Qu.:0.000000 Max. :0.0544748 Max. :0.321345
In [100]:
table(cells.Sul.6mo$time.from.exposure.months)
table(cells.Sul.18mo$time.from.exposure.months)
0 6 104 79
0 18 272 64
In [101]:
CD4_LMM_6mo = lmer(CD4_positive_alpha_beta_T_cell ~ time.from.exposure.months + site + (0+time.from.exposure.months|site) + (1|patient) + (0 + time.from.exposure.months|patient), cells.Sul.6mo)
summary(CD4_LMM_6mo)
lmerTest::anova(CD4_LMM_6mo)
NK_LMM_6mo = lmer(NK_cell ~ time.from.exposure.months + (1|patient) + site + (0+time.from.exposure.months|site) + (0 + time.from.exposure.months|patient), cells.Sul.6mo)
summary(NK_LMM_6mo)
lmerTest::anova(NK_LMM_6mo)
CD4_LMM_18 = lmer(CD4_positive_alpha_beta_T_cell ~ time.from.exposure.months + site + (0+time.from.exposure.months|site) +
(1|patient) + (0 + time.from.exposure.months|patient),
cells.Sul.18mo)
summary(CD4_LMM_18)
lmerTest::anova(CD4_LMM_18)
NK_LMM_18 = lmer(NK_cell ~ time.from.exposure.months + site + (0+time.from.exposure.months|site) +
(1|patient) + (0 + time.from.exposure.months|patient),
cells.Sul.18mo)
summary(NK_LMM_18)
lmerTest::anova(NK_LMM_18)
Linear mixed model fit by REML t-tests use Satterthwaite approximations to
degrees of freedom [lmerMod]
Formula: CD4_positive_alpha_beta_T_cell ~ time.from.exposure.months +
site + (0 + time.from.exposure.months | site) + (1 | patient) +
(0 + time.from.exposure.months | patient)
Data: cells.Sul.6mo
REML criterion at convergence: 1116
Scaled residuals:
Min 1Q Median 3Q Max
-2.27082 -0.46741 0.01641 0.53162 2.23743
Random effects:
Groups Name Variance Std.Dev.
patient time.from.exposure.months 0.00 0.000
patient.1 (Intercept) 13.39 3.659
site time.from.exposure.months 0.00 0.000
Residual 14.39 3.793
Number of obs: 183, groups: patient, 148; site, 2
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 27.7925 0.9254 146.3400 30.034 < 2e-16 ***
time.from.exposure.months 0.3070 0.1126 79.2900 2.726 0.00788 **
siteMRC 1.3659 1.0115 131.7300 1.350 0.17921
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) tm.f..
tm.frm.xps. -0.260
siteMRC -0.838 -0.056
| Sum Sq | Mean Sq | NumDF | DenDF | F.value | Pr(>F) | |
|---|---|---|---|---|---|---|
| time.from.exposure.months | 106.94330 | 106.94330 | 1 | 79.29353 | 7.432645 | 0.007881019 |
| site | 26.23795 | 26.23795 | 1 | 131.72634 | 1.823558 | 0.179205805 |
Linear mixed model fit by REML t-tests use Satterthwaite approximations to
degrees of freedom [lmerMod]
Formula: NK_cell ~ time.from.exposure.months + (1 | patient) + site +
(0 + time.from.exposure.months | site) + (0 + time.from.exposure.months |
patient)
Data: cells.Sul.6mo
REML criterion at convergence: 1116
Scaled residuals:
Min 1Q Median 3Q Max
-2.6040 -0.3983 0.1381 0.5411 1.5027
Random effects:
Groups Name Variance Std.Dev.
patient time.from.exposure.months 0.00 0.000
patient.1 (Intercept) 10.10 3.178
site time.from.exposure.months 0.00 0.000
Residual 17.07 4.132
Number of obs: 183, groups: patient, 148; site, 2
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 17.85846 0.90868 157.45000 19.653 <2e-16 ***
time.from.exposure.months -0.01896 0.11717 112.11000 -0.162 0.872
siteMRC 1.53639 0.98826 145.31000 1.555 0.122
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) tm.f..
tm.frm.xps. -0.278
siteMRC -0.832 -0.058
| Sum Sq | Mean Sq | NumDF | DenDF | F.value | Pr(>F) | |
|---|---|---|---|---|---|---|
| time.from.exposure.months | 0.4467516 | 0.4467516 | 1 | 112.1093 | 0.02617279 | 0.8717703 |
| site | 41.2551393 | 41.2551393 | 1 | 145.3133 | 2.41691831 | 0.1222061 |
Linear mixed model fit by REML t-tests use Satterthwaite approximations to
degrees of freedom [lmerMod]
Formula: CD4_positive_alpha_beta_T_cell ~ time.from.exposure.months +
site + (0 + time.from.exposure.months | site) + (1 | patient) +
(0 + time.from.exposure.months | patient)
Data: cells.Sul.18mo
REML criterion at convergence: 2133.7
Scaled residuals:
Min 1Q Median 3Q Max
-3.1729 -0.5151 0.0875 0.5988 2.2991
Random effects:
Groups Name Variance Std.Dev.
patient time.from.exposure.months 0.01779 0.1334
patient.1 (Intercept) 9.69878 3.1143
site time.from.exposure.months 0.00000 0.0000
Residual 23.31752 4.8288
Number of obs: 336, groups: patient, 301; site, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 27.61383 1.04539 319.20000 26.415 < 2e-16 ***
time.from.exposure.months 0.05880 0.04554 80.50000 1.291 0.20041
siteMRC 1.62729 1.18780 306.30000 1.370 0.17169
siteSUN -3.04998 1.11650 312.90000 -2.732 0.00666 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) tm.f.. sitMRC
tm.frm.xps. -0.162
siteMRC -0.858 0.004
siteSUN -0.922 0.065 0.803
| Sum Sq | Mean Sq | NumDF | DenDF | F.value | Pr(>F) | |
|---|---|---|---|---|---|---|
| time.from.exposure.months | 38.86183 | 38.86183 | 1 | 80.49473 | 1.666637 | 2.004050e-01 |
| site | 1006.21490 | 503.10745 | 2 | 302.18827 | 21.576377 | 1.741754e-09 |
Linear mixed model fit by REML t-tests use Satterthwaite approximations to
degrees of freedom [lmerMod]
Formula:
NK_cell ~ time.from.exposure.months + site + (0 + time.from.exposure.months |
site) + (1 | patient) + (0 + time.from.exposure.months | patient)
Data: cells.Sul.18mo
REML criterion at convergence: 1950.4
Scaled residuals:
Min 1Q Median 3Q Max
-3.03343 -0.46025 0.04702 0.49411 1.70801
Random effects:
Groups Name Variance Std.Dev.
patient time.from.exposure.months 0.02905 0.1705
patient.1 (Intercept) 7.80758 2.7942
site time.from.exposure.months 0.00000 0.0000
Residual 10.40439 3.2256
Number of obs: 336, groups: patient, 301; site, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 17.96554 0.79393 314.76000 22.629 < 2e-16 ***
time.from.exposure.months -0.06091 0.03696 72.72000 -1.648 0.10368
siteMRC 1.43402 0.90614 306.50000 1.583 0.11455
siteSUN 2.65475 0.84977 310.38000 3.124 0.00195 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) tm.f.. sitMRC
tm.frm.xps. -0.137
siteMRC -0.861 0.008
siteSUN -0.925 0.060 0.804
| Sum Sq | Mean Sq | NumDF | DenDF | F.value | Pr(>F) | |
|---|---|---|---|---|---|---|
| time.from.exposure.months | 28.25535 | 28.25535 | 1 | 72.72003 | 2.715706 | 0.103679407 |
| site | 126.87598 | 63.43799 | 2 | 301.19221 | 6.097216 | 0.002536373 |
Figure 5A¶
In [76]:
testTheme = list(geom_boxplot(outlier.shape=NA, coef=0),
geom_jitter(aes(fill=factor(time.from.exposure.months)), position=position_jitter(0.15), shape=21, size=2),
scale_fill_manual(values=c("white", "darkgrey")),
theme_classic(),
labs(x="Months from Baseline"),
theme(plot.title = element_text(hjust = 0.5, size=14, face="bold")),
theme(axis.text.x=element_text(size=12, color="black"),
axis.text.y=element_text(size=10, color="black"),
axis.title=element_text(size=12)),
theme(legend.position="none"))
In [102]:
CD4_cell_6mo = ggplot(cells.Sul.6mo , aes(x=as.factor(time.from.exposure.months), y=CD4_positive_alpha_beta_T_cell)) + testTheme +
labs(y="CD4+ T Cell (%)") + geom_signif(comparisons=list(c("0", "6")),
annotations = c("p = 0.0079"),
step_increase = 0.09, vjust = 0.14,
tip_length = 0.015, textsize=3.5)
CD4_cell_6mo
Figure 5B¶
In [103]:
CD4_cell_18mo = ggplot(cells.Sul.18mo , aes(x=as.factor(time.from.exposure.months), y=CD4_positive_alpha_beta_T_cell)) + testTheme +
labs(y="CD4+ T Cell (%)") + geom_signif(comparisons=list(c("0", "18")),
annotations = c("p = 0.20"),
step_increase = 0.09, vjust = 0.14,
tip_length = 0.015, textsize=3.5)
CD4_cell_18mo
Figure 5C¶
In [104]:
NK_cell_6mo = ggplot(cells.Sul.6mo , aes(x=as.factor(time.from.exposure.months), y=NK_cell)) + testTheme +
labs(y="NK Cell (%)") + geom_signif(comparisons=list(c("0", "6")),
annotations = c("p = 0.87"),
step_increase = 0.09, vjust = 0.14,
tip_length = 0.015, textsize=3.5)
NK_cell_6mo
Figure 5D¶
In [105]:
NK_cell_18mo = ggplot(cells.Sul.18mo , aes(x=as.factor(time.from.exposure.months), y=NK_cell)) + testTheme +
labs(y="NK Cell (%)") + geom_signif(comparisons=list(c("0", "18")),
annotations = c("p = 0.10"),
step_increase = 0.09, vjust = 0.14,
tip_length = 0.015, textsize=3.5)
NK_cell_18mo
